Energy Control and Generation Method and System

ABSTRACT

A method and system of controlling the time dependent transfer of electrical power between a first electrical network and a second electrical network is disclosed. The first electrical network is operable to provide instantaneous electrical power to the second electrical network located at a location, the second electrical network includes electrical generating capacity at the location based on stored energy accessible at die location. The method and system involves receiving at the second electrical network pricing information from the first electrical network, the pricing information associated with the future supply of electrical power by the first electrical network to the second electrical network and then modifying substantially in real time the transfer of electrical power between the first and second electrical networks in accordance with the pricing information and the electricity demand characteristics of the location.

PRIORITY DOCUMENTS

The present application claims priority from Australian ProvisionalPatent Application No 2013903300 titled “ENERGY CONTROL AND GENERATIONMETHOD AND SYSTEM” and filed on 29 Aug. 2013, the content of which ishereby incorporated by reference in its entirety.

INCORPORATION BY REFERENCE

The following co-pending patent application is referred to in thefollowing description: PCT Application No PCT/AU2014/00060S titled“ELECTRICAL POWER CONTROL METHOD AND SYSTEM” and filed on 12 Jun. 2014claiming priority from Australian Provisional Application No 2013902126.

The content of this application is hereby incorporated by reference inits entirety.

TECHNICAL FIELD

The present invention relates to the supply of electrical power by anelectrical power network. In a particular form, the present inventionrelates to the methods and systems that may be adopted by an end-user ofan electrical power network to manage the cost of electrical power tothe end-user.

BACKGROUND

Electricity supply and pricing in a utility scale “smart grid” offerssignificant improvements in energy security, reliability, efficiency andlower cost to end-users. Unfortunately, grid generated supply ofelectrical power must equal demand instantaneously, as large scalestorage and buffering of electricity is not economically feasible. Ingeneral, there are a large number of utility scale generation plantsinterconnected via complex wired distribution networks fortransportation of electricity to spatially diverse end-users. For asmart grid spanning several distinct demand regions there exists theopportunity to trade surplus generated capacity or acquire additionalcapacity that had not been accounted for in a region's forecasted demandprofile and resulting scheduled generation. This is seen as a primeadvantage of the network based energy supply model.

Referring now to FIG. 1, there is shown a schematic representation of asmart grid 100 of the type described above. Physical electrical power isgenerated and transported to the end-user 130 by transmission anddistribution networks 115, 120 with monies exchanged between thegenerator 110 and retailer 105 via a central clearing entity being themarket operator 150. The end-user 130 interacts only with the retailer105 for cost recovery as indicated. The end-user 130 consumption ismonitored via a measuring device MD which is used by the retailer 105 toinvoice the end-user 130. Even with the electricity market structured asdescribed, the actual temporal wholesale spot price varies considerablyand is typically not visible to the end-user 130.

A given retailer 105 participating in the smart grid and representing agroup of end-users, must therefore develop a risk based tariff scheduleto reflect the cost of supplying electricity to a captive group ofend-users. The tariff is generally calculated to be of acceptable riskover a time period of months. The final retail cost (RC) presented tothe end-user by the retailer comprises: the anticipated purchase cost ofelectricity directly from the smart grid market operator (WC),transmission network cost (TNC), distribution network cost (DNC) fromthe grid to the physical region of the end-user, end-user metering costs(MRC), retail operating (ROPC) costs (including, hedging future funds,market participation costs, credit notes for market purchases, customerbilling and marketing) and retailer profit margin (RPM). Other costssuch as government levies (GL) and environmental schemes (ESC) andfeed-in tariff costs (FITC) are also passed through to the end-user.

That is, the total retail cost RC presented to the end-user is thus:

RC=WC+TNC+DNC+MRC+ROPC+RPM+FITC+ESC+GL

Approximate proportions of the components comprising the RC are: WC=25%,TNC=26%. DNC=31%, (ROPC+RPM)=12%, FITC<0.5%, CL=1.5%, ESC=3%. This datais representative of the Australian 2012/2013 electricity cost and isnot expected to vary substantially from other smart grid type networksin developed markets. Clearly, the total network costs constitute amajor share (57%).

The only time varying costs exposed to the retailer are the WC and ROPCdue to direct exposure to the real-time electricity market. Networkaccess costs are in general fixed over a period of several years due toreturn on investment of huge capital infrastructure. However, futuremarket innovation is likely to augment transmission network costs to bereflective of the actual power transferred between nodes comprising thetransmission network.

Referring now to FIG. 2, there is shown a pie chart of the decompositionof end-user costs for in this case a typical Australian end-userparticipating in the smart grid as referred to above. As can be seen,the majority of the final end-user cost remitted to the retailer goes tothe electrical transmission 235 and distribution networks 205 ascompared to government levies 210, retailer operating costs and profitmargin 215, environment costs 220, feed in tariff costs 225 andelectricity wholesale costs 230. All present day electricity costforecasts as of 2013 indicate that the future trend in price escalationsis likely to be primarily driven by increasing network costs.

As would be appreciated, the smart grid electrical network of the typedepicted in FIG. 1 provides a means for matching supply to forecasteddemand. Referring now to FIG. 3, there is shown actual forecast,dispatched and settled spot pricing data from the Australia nationalsmart grid. The upper chart of FIG. 3 shows the day-ahead forecast spotprice 305 (circles) ahead of present time 315 broadcast to the smartgrid participants and updated every 30 mins and where the black squaredata points 325 show the actual 5 min settled spot price. The lowerchart shows the actual dispatched generation power 330. Clearly, in thisexample the forecasted 310 and dispatched 330 generated power suppliedto the regional network are well matched. However, as can be seen fromthe upper chart, significant price volatility is observed even within a48 hour period. That is, even though the smart grid forecasting enablesaccurate tracking of supply to demand, the actual settled spot pricethat is cleared by the market operator is open to considerable pricefluctuations beyond those forecasted.

In some instances, such as where there are extreme weather conditions,the spot price exceeds limits placed by the market operator and presentsconsiderable exposure to the retailer. This considerable risk can bepartially mitigated by the use of hedging contracts by retailers withgenerators and/or the use of futures contracts. Even so, the retailermust provide a competitive tariff rate to an end-user otherwise retailerswitching will occur by an end-user in a deregulated retail market.Increased retailer competition is seen as a market response forimproving market operation. However, every retailer is still exposed tothe time varying nature of the wholesale market. It is anticipated thatincreased integration of renewable resources within the smart grid willfurther increase wholesale market volatility for the reasons outlinedbelow.

Generally, it is the primary goal of an electrical power supply networkto match supply to demand. This is a challenging task because demand maybe highly time varying. The utility company or generators thereforeneeds to provision enough generation, transmission and distributioncapacities for peak demand rather than for simply the average demand. Asa result, the power network has a low load factor and is underutilisedmost of the time, this resulting in a disproportionately high cost tothe end-user. For example, in Australia and the United States, thenational load factors are approximately 55% with 10% of the generationcapacity and 25% of the distribution facilities being used less than100-400 hours per year. That is, peak demand capacity is used less than2-5% of the time.

Shaping the demand to reduce the peak and to smooth any variations canpotentially greatly improve the electrical power supply network'sefficiency and provide enormous savings. An alternative strategy toimprove efficiency and reduce cost is to conversely match demand to thesupply. As the proportion of renewable sources (eg, solar and windpower) steadily rises, the electrical power supply will also become evenmore highly time varying. As a result, matching demand to the availablesupply may therefore become a more effective way to improve the overallefficiency of the electrical power supply network and reduce cost.

As referred to previously, large scale renewable energy electricitygeneration integrated into the smart grid creates new challenges. Takingthe Australian national smart grid as an example, despite the relativelylow running average regional wholesale electricity prices as can be seenin FIG. 3, there has been significant increase in the spot price marketvolatility as a result of the introduction of electricity generationbased on renewable energy, especially in those regional zones within thesmart grid that physically host the large scale renewable energy plants.Interconnection of networks and renewables must be scheduled andaccordingly there exists further demands placed on network capacity toprovide for opportunistic access of renewable generation once it comeson line. As an example, South Australia has the highest penetration ofwind generation (>2.5 GW_(e) name-plate capacity) in the Australiansmart grid. Correspondingly, the electricity market has observed risingincidence of negative pool prices in South Australia.

Australia, like many developed electricity markets is increasing itsadoption of wind generation based on environmental concerns. FIG. 4shows actual (but typical) data of the regional pool price correlated tothe instances of dispatched wind generation in South Australia over a24-hour period. Wind generators bid low and also bid often at slightlynegative prices to ensure dispatch of the generated electrical power. Asrenewables in Australia receive the value of renewable energycertificates (RECs) in addition to spot market returns, they are able toafford to bid preferentially low on the basis of this cost advantage.

As a result of analysis carried out by the Applicant, it has been foundthat all instances of South Australian prices that were significantlybelow zero in 2011-12 (including prices around the −$1000 market floor)were associated with the strategic bidding or rebidding of windgenerated power where the market is essentially compelled to accept thewind generated power. Taking South Australia as an example, it has beenobserved on several occasions that a particular renewable energygeneration participant's bidding strategy in South Australia haseffectively shut down other generators (including other wind generatorfarms and open cycle gas turbine (OCGT) gas-peakers and combined cyclegas turbine (CCGT) plants).

Negative pricing events have the effect of collapsing the electricitymarket price as wind energy is dispatched in the electricity supplynetwork. Wind energy electricity generation also requires regionalgas-peaker plants to buffer the wind supply when dispatched. As such,there is an increasing requirement for gas-peaker plants to be scheduledin parallel with semi-dispatchable wind farm generation. However, themarket risk for longer term supply is the uneconomic operation ofgas-fired peakers due to the low pool prices generated and associatedwith the supply of electricity by wind energy. This represents a largerisk to both the retailer and end-user for future price stability astraditional gas fired base load plants that are unconnected to wind farmgeneration will become uneconomic.

Spot price volatility therefore causes market uncertainty and can affectthe efficient dispatch of generation within the smart grid. Theincidence of counter-price export flows where every generator in aregional pool is not compensated also poses difficulties for retailersand smaller generators seeking to hedge against volatility, especiallyacross regions through inter-regional settlement residue auctionsattempting to settle on payments between regions. The conditionsoutlined above therefore create high risk for generators and reducecompetition among generators in adjoining regions. The additional riskscan deter new entry and investment in both generation and retail,leading ultimately to higher costs that consumers ultimately bear.

There is therefore a need to provide end-users of the electrical supplymarket with options to interact with the smart grid or electrical supplynetwork to reduce their exposure to price increases and volatility.

SUMMARY

In a first aspect the present invention accordingly provides a methodfor controlling the time dependent transfer of electrical power betweena first electrical network and a second electrical network, the firstelectrical network operable to provide instantaneous electrical power tothe second electrical network located at a location, the secondelectrical network including electrical generating capacity at thelocation based on stored energy accessible at the location, the methodcomprising:

receiving at the second electrical network pricing information from thefirst electrical network, the pricing information associated with thefuture supply of electrical power by the first electrical network to thesecond electrical network; and

modifying substantially in real time the transfer of electrical powerbetween the first and second electrical networks in accordance with thepricing information and electricity demand characteristics of thelocation.

In another form, modifying substantially in real time the transfer ofelectrical power between the first and second electrical networkincludes the second electrical network generating electricity on-site tosatisfy the electricity demand characteristics of the second electricalnetwork where a cost of generating electricity on-site is less than orequal to a cost of electricity supplied by the first electrical network.

In another form, modifying substantially in real time the transfer ofelectrical power between the first and second electrical networkincludes the second electrical network supplying at least a portion ofthe on-site generated electricity to the first electrical network at areimbursement price greater than or equal to a cost of generatingelectricity on-site.

In another form, modifying substantially in real time the transfer ofelectrical power between the first and second electrical networkincludes storing by the second electrical network electricity suppliedby the first electrical network or generated by the second electricalnetwork to be either employed by the second electrical network orsupplied back to the first electrical network at a later time.

In another form, the stored energy is in the form of combustible gas.

In another form, the combustible gas is stored at the location and iscomprised of any one of:

compressed natural gas (CNG);

liquefied propane gas (LPG);

liquefied natural gas (LNG); or

any combination of the above.

In another form, the combustible gas is supplied by a gas supplynetwork.

In another form, modifying substantially in real time the transfer ofelectrical power between the first and second electrical networkincludes receiving pricing information from the gas supply network andincluding this information in present and future calculation ofgenerating electricity on-site.

In another form, the method further includes supplying a forecast of gasfuel consumption by the second electrical network to the gas supplynetwork.

In another form, the electrical generating capacity at the locationbased on stored energy is an on-site gas-to-electricity converter.

In another form, the on-site gas-to-electricity converter comprises agas-to-rotational energy converter and a rotationalenergy-to-electricity converter.

In another form, the gas-to-rotational energy converter is an internalcombustion reciprocating engine (ICRE).

In another form, the first electrical network is an electricallyinterconnected utility-scale grid under the control of a market operatorcomprising at least one power generation source and a transmissionand/or distribution interconnection network operable to supply power.

In a second aspect the present invention accordingly provides anelectrical power control system comprising:

a first electrical network configured to supply instantaneous electricalpower to a second electrical network located at a location, wherein thefirst electrical network further provides pricing information associatedwith the future supply of electricity;

a power measuring device for measuring the demand characteristics of thesecond electrical network;

an on-site stored energy-to-electricity converter for converting storedenergy at the location to electricity; and

a controller for receiving the demand characteristics of the secondelectrical network and the pricing information from the first electricalnetwork and determining a power transfer schedule for the secondelectrical network controlling whether electricity is to be sourced fromthe first electricity network or from the on-site storedenergy-to-electricity converter.

In another form, the power transfer schedule further controls whetherelectricity is to be stored on-site by the second electrical network.

In another form, the power transfer schedule further controls whetherelectricity from the on-site energy-to-electricity converter is suppliedto the first electrical network.

In another form, the on-site stored energy-to-electricity converter is agas-to-electricity converter based on combustible gas.

In another form, the combustible gas is supplied by a gas supplynetwork.

In another form, the combustible gas is stored at the location and iscomprised of any one of:

compressed natural gas (CNG);

liquefied propane gas (LPG);

liquefied natural gas (LNG); or

any combination of the above.

In a third aspect the present invention accordingly provides a method ofcontrolling the interaction between first and second power supplysystems at an end-user site, the first power supply system operable toprovide electrical power that matches the instantaneous demand of aplurality of end-users including the end-user site, and where the secondpower supply system is based on stored energy where the end-user is ableto generate electrical power at the end-user site from the stored energyat a predetermined time and for a predetermined duration, the methodcomprising:

dynamically switching by the end-user between the first and second powersupply systems in accordance with a cost benefit analysis based onpricing information provided substantially in real time to the end-userby the first power supply system.

In another form, the stored energy is based on combustible gas.

In another form, the combustible gas is supplied by a gas supplynetwork.

In another form, combustible gas is stored at the location and iscomprised of any one of:

compressed natural gas (CNG);

liquefied propane gas (LPG);

liquefied natural gas (LNG); or

any combination of the above.

In a fourth aspect the present invention accordingly provides anelectricity market system comprising:

a plurality of electricity generators;

a distribution network for distributing electricity generated by theplurality of electricity generators to a plurality of end-users orcustomers of the electricity market;

at least one retailer for receiving monies from the plurality ofend-users in satisfaction for the electricity supplied to an end-user,and

a market operator for determining a forecast demand and a settled pricefor the wholesale cost of electricity as supplied by the plurality ofgenerators, wherein the market includes a plurality of end-users eachenabled with on-site electrical generating capacity based on storedenergy and who determine whether to receive electricity from theelectricity market or supply electricity to the electricity market basedon a cost benefit analysis carried out by the end-user.

In another form, the electrical generating capacity based on storedenergy is a gas-to-electricity converter based on the supply ofcombustible gas to the individual end-user.

In another form, the combustible gas is supplied by a gas supplynetwork.

In another form, the combustible gas includes combustible gas stored atan individual user's location, the stored combustible gas comprised ofany one of:

compressed natural gas (CNG):

liquefied propane gas (LPG);

liquefied natural gas (LNG); or

any combination of the above.

In another form, the electricity market system further includes anensemble of end-users having an aggregated on-site generating capacitywho are treated as part of the electricity generating capacity of theelectricity market to manage demand volatility.

In a fifth aspect the present invention accordingly provides an energysupply system comprising:

a stored energy network comprising combustible gas supplied to anend-user by a gas distribution network for supplying combustible gas,the combustible gas metered by a gas provider and supplied at an agreedgas consumption tariff structure; and

an instantaneous energy network comprising an electrical distributionnetwork that supplies electricity to the end-user site that is meteredby an energy provider at an agreed electricity consumption tariffstructure, wherein the end-user switches between electricity generatedon-site from the combustible gas and electricity supplied by theelectrical distribution network to minimise the end-user costs.

In another form, the combustible gas is gas supplied by a gas supplynetwork to individual end-users.

In another form, the end-user provides forecast gas consumption demandinformation to the gas distribution network to negotiate a future gasconsumption tariff structure.

In another form, the future gas consumption tariff structure is time andvolume dependent.

In another form, the end-user provides forecast electricity consumptioninformation to the electrical distribution network to negotiate a futureelectricity consumption tariff structure.

In another form, the future electricity consumption tariff structure istime and amount dependent.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the present invention will be discussed with reference tothe accompanying drawings wherein:

FIG. 1 is a schematic representation of a smart grid under theadministration of a market operator comprising a first electricalnetwork including electricity generation sources, transmission networks,a retailer and an end-user illustrating the physical electrical powerflow and the transaction cost arrangements;

FIG. 2 is a pie chart of the representative component costs of theend-user cost based on the Australian electricity market over 2012/2013;

FIG. 3 depicts representative look-ahead forecast data for a regionalzone of a smart grid. The upper chart shows the forecast pool price dataat 30 min interval resolution (circles) and the actual settled spotprices every 5 mins (squares) over an extended period. The lower chartsimilarly shows the forecast and actual demand dispatched electricityover the same period (data taken from the Australian smart grid forSouth Australia region zone);

FIG. 4 is a representative actual Australian electricity market data forregion pool price versus actual wind generation dispatched in SouthAustralia to the smart grid over a 24-hour period;

FIG. 5 is a schematic representation of an analogous dynamic responsemodel of a smart grid using a ball-spring model. Upper model includes asmart grid having generation, network distribution and retailing withthe lower model further including a constrained cost of response of anend-user;

FIG. 6 is a schematic functional diagram of the components of a smartgrid in accordance with an illustrative embodiment including a secondelectrical or end-user network having access to a combustible gassupply;

FIG. 7 is a schematic representation of standard electrical and gassupply networks for energy supply to an end-user;

FIG. 8 is a schematic representation of a smart grid illustrating thebidirectional interchange between smart grid participants and theunidirectional information available to end-users;

FIG. 9 is a schematic representation of a system for transferringelectricity between a first electrical network and an end-userelectrical network in accordance with an illustrative embodiment wherethe end-user electrical network is also connected to a pressurised gasnetwork;

FIG. 10 is a schematic representation of an ensemble or collection ofend-users each having a respective end-user electrical networkinteracting with a first electrical network;

FIG. 11 is a schematic representation of a system for the transfer ofelectrical power between a first electricity network and a second orend-user electricity network according to another illustrativeembodiment;

FIG. 12 is a schematic representation of a system for transfer ofelectrical power between a first electrical network and multipleend-user electrical networks organised in districts according to anillustrative embodiment;

FIG. 13 is a detailed schematic representation of a system fortransferring electrical power between a first electrical network and anend-user network coupled to both the first electrical network and acombustible gas supply grid according to an illustrative embodiment;

FIG. 14 is a graph of example pricing information in the form offorecast time dependent cost or pricing data for a demand region (ieSouth Australia) of a smart grid in Australia (ie the National EnergyMarket (NEM) managed by the Australian Energy Market Operator (AEMO));

FIG. 15 is a graph of example pricing information in the form ofend-user forecast time-of-use pricing as would be provided by the firstelectrical network retailer based on the regional wholesale forecastdata illustrated in FIG. 14;

FIG. 16 is a graph of the forecast retail cost to an end-user for supplyof electricity from the smart grid as a function of time of daydepicting the threshold for switching between on-site generation and thefirst electrical network for η_(gen)=32.5% and M_(gas)=3.5;

FIG. 17 is a graph of the forecast retail cost to an end-user for supplyof electricity from the smart grid as a function of time of daydepicting the threshold for switching between on-site generation and thefirst electrical network for η_(gen)=35% and M_(gas)=5;

FIG. 18 is a graph of the forecast retail cost to an end-user for supplyof electricity from the smart grid as a function of time of daydepicting the threshold for switching between on-site generation and thefirst electrical network for η_(gen)=45% and M_(gas)=3.5;

FIG. 19 is a graph of the forecast retail cost to an end-user for supplyof electricity from the smart grid as a function of time of daydepicting the threshold for switching between on-site generation and thefirst electrical network for η_(gen)=45% and M_(gas)=5;

FIG. 20 is a graph of a NPV analysis as a function of years of servicefor an on-site gas-to-electricity converter;

FIG. 21 depicts on the left hand side a detailed schematicrepresentation of the gas-to-electricity converter illustrated in FIG. 6in the form of an internal combustion reciprocating engine (ICRE) andalternator arrangement and on the right hand side a graph of thealternating current frequency as a function of shaft rotation producedby the alternator for varying numbers of pole pairs;

FIG. 22 is a schematic representation of system for transfer ofelectrical power between a first electrical network and a secondelectrical network further incorporating a gas supply network for use byan on-site gas-to-electricity converter in accordance with anillustrative embodiment;

FIG. 23a is a schematic representation of a system for transfer ofelectrical power between a first electrical network and a secondelectrical network further incorporating a gas supply network for use byan on-site gas-to-electricity converter in accordance with anotherillustrative embodiment;

FIG. 23b is schematic representation of the functional components of agas-to-electricity converter according to an illustrative embodiment;

FIG. 23c is a schematic circuit diagram of the transfer switchillustrated in FIG. 23 a;

FIG. 24 is a contour plot of the on-site electricity generation cost asa function of the specific gas-to-electricity conversion efficiencyrange 25≦η_(Gen)≦60% and methane fuel cost retail to wholesale costmultiple between 1 to 10;

FIG. 25 is a contour plot of the on-site electricity generation cost asa function of the specific gas-to-electricity conversion efficiencyrange 25≦η_(Gen)≦60% and methane fuel cost retail to wholesale costmultiple between 1 to 5;

FIG. 26 is a contour plot of the on-site electricity generation cost asa function of the specific gas-to-electricity conversion efficiencyrange 30≦η_(Gen)≦40% and methane fuel cost retail to wholesale costmultiple between 1 to 5;

FIG. 27 is a graph of the generation efficiency of electricity as afunction of electrical load for an ICRE based gas-to-electricityconverter using methane fuel based on different generator configurationswhere the inset schematic demonstrates the effect of employing aparallel connected group of generators to provide improved partial loadperformance for a given electrical load;

FIG. 28 is a contour plot of on-site electricity generation cost as afunction of partial electrical load and methane fuel cost for a givengas-to-electricity full load conversion efficiency of η_(Gen)=35% forP_(Gen)=10 kW_(e) where the methane fuel cost is given as an end-userretail cost multiple over the wholesale gas cost;

FIG. 29 is a contour plot of on-site electricity generation cost as afunction of partial electrical load and methane fuel cost for a givengas-to-electricity full load conversion efficiency of η_(Gen)=40% forP_(Gen)=10 kW_(e);

FIG. 30 is a contour plot of on-site electricity generation cost as afunction of partial electrical load and methane fuel cost for a givengas-to-electricity full load conversion efficiency of η_(Gen)=45% forP_(Gen)=10 kW_(e);

FIG. 31 is a schematic representation of a smart grid under theadministration of a market operator similar to that illustrated in FIG.1 but where an end-user is compensated by a retailer for providingelectricity generated on-site and provided in one example to a “feed-in”retailer to stabilise regional peak demand;

FIG. 32 is a functional block diagram of a power transfer systemaccording to an illustrative embodiment incorporating a heat recoverymodule;

FIG. 33 is a functional block diagram of a power transfer systemaccording to a further illustrative embodiment incorporating a normaltime of use load and an off-peak load;

FIG. 34 is a functional block diagram of a power transfer systemaccording to an illustrative embodiment incorporating a unitary powermeasuring and metering device capable of bi-directional power flowmeasurement and time logging;

FIG. 35 is a functional block diagram of a power transfer systemaccording to another illustrative embodiment including an emissionreduction device for emissions from the gas-to-electricity converter;

FIG. 36 is a functional block diagram of a power transfer systemaccording to an illustrative embodiment where pricing information may beaccessed from a publicly available database and/or by direct access to aretailer database;

FIG. 37 is a graph of an example end-user or second electrical networktime-of-use power transfer schedule in accordance with an illustrativeembodiment calculated based on first electrical network time-of-useelectrical forecast;

FIG. 38a is a graph of costs avoided by an end-user for the powertransfer schedule based on the first electrical network forecastillustrated in FIG. 37 as a function of end-user gas-to-electricityconversion costs (curves are 3815 (η_(Gen)=45%), 3810 (η_(Gen)=50%) &3805 (η_(Gen)=60%));

FIG. 38b is a schematic representation of a smart grid incorporating anensemble of end-users each operating in accordance with an illustrativeembodiment depicting the information flow between the end-users and thefirst electrical network; and

FIG. 39 is a functional block diagram of an example computer system thatmay be adopted to implement illustrative embodiments described below.

In the following description, like reference characters designate likeor corresponding parts throughout the figures.

DESCRIPTION OF EMBODIMENTS

Referring now to FIG. 5, there is shown a simplified analogous dynamicmodel of the smart grid 400 represented as a one-dimensional (ID) ball &spring structure. The system is constrained by rigid endpoints 405 &445. The centre-of-mass positions and velocities (x_(j), v_(i))represent particular smart grid parameters (viz., price and demand) andthe masses 415, 425 and 435 represent the magnitude of generators,networks and retailers forming the smart grid. Forcing functions 416(generation), 426 (network distribution) and 436 (retailing) drive themasses of the system and the coupling constants are represented byelastic spring constants 410, 420, 430 and 440. The spring constants andmass inertia therefore represent the time constants of the system.Analysis of actual historical data for demand and settled costtime-sequences can be provided by applying a Fourier transform in orderto understand underlying frequency modes of the smart grid 400.

A 3-body system as shown in the model 400 of FIG. 5 can be used tounderstand gross behaviour of the smart grid. In general, modelling ofan electrical supply network assumes an end-user as an unsophisticatedprice taker and is not included in any system response modelling. Aswill become apparent in the various illustrative embodiments describedbelow, these embodiments include yet another component in the dynamicresponse representing an end-user 495 which interacts and providesfeedback to the smart grid or first electrical network. As an example,consider the 4-body model 450 depicted in the lower model of FIG. 5where the end-user is coupled to the smart grid system (ie the left handside of lower model which is analogous to upper model 400) and isrepresented as a further mass 495 having associated spring constants 496& 490. The additional feature is a physical constraint representinglimits to price taking or demand. The positions of the end-user limits497 and 498 are determined by the end-user network, for example, theon-site generation cost decision points which will be described below.This end-user demand response translates to a participative forcingfunction 494 which will affect the overall behaviour of the smart gridnetwork 450 as will be discussed below.

In accordance with an illustrative embodiment, an end-user may coupleadvantageously to a dynamic smart grid. In accordance with thisembodiment, an end-user may advantageously price-take from a smart gridif there exists means to modify energy consumption by one or more of thefollowing strategies including.

-   -   a) utilising an alternative energy source of known cost        structure; or    -   b) shifting a time-dependent energy consumption of the end-user        to lower time-of-use cost; or    -   c) shifting a time dependent energy injection from the end-user        to the smart grid to achieve a higher time-of-use feed-in        tariff.

Throughout the specification a “smart grid” is defined to include anintegrated electrical power generation and electrical power transportnetwork having a market operator that provides time-dependent pricinginformation presented in one embodiment as a forecast to the primarysmart grid participants. Upon this forecast, market forces interact viaretailers to produce an outcome of providing end-users with highreliability energy with time-dependent supply substantially matchinginstantaneous demand.

Referring now to FIG. 6, there is shown a functional diagram of thecomponents of a power transfer system in accordance with an illustrativeembodiment. In this example, the smart grid comprises a first electricalnetwork 510 that is physically connected at 525 and 530, distributed tothe end-user site by links 515 and 520 which may the same physicalconnection. Electricity consumed and delivered from first electricalnetwork 510 is metered by power measuring devices 535 and 540 (which mayalso be physically contained within the same device) for consumption orfeed-in by the second or end-user electrical network 500, respectively.Metered electrical power is delivered to a second electrical networkpower transfer system 563 which can switch power from the firstelectrical network to the second electrical network load 570.Alternatively, metered power 515 from the first network can be deliveredvia power transfer module 562 to an energy storage device 573.

The end-user or second electrical network 500 is further connected to ahydrocarbon gas-grid or combustible gas network 505 via physicalconnection 545 which provides combustible gas feedstock 555 via a gasmetering device 550 recording consumption of gas. In another embodiment,gas feedstock may be containerised in the form of bottled gas orsimilar. Second or end-user electrical network 500 further includes agas-to-electricity converter 560 which generates on-demand electricalpower 561 that is connected to power transfer devices 562 and 563.Electrical power sourced from at least one of the first electricalnetwork 510 or on-demand on-site generator 560 or on-demand on-sitestored power sources 575 can be provided to the end-user load 570 or fedback into the first electrical network 510.

In this illustrative embodiment power source 575 is comprised of anelectricity-to-energy storage module 573, and a storedenergy-to-electricity conversion module 574. In one example, storagemodule 573 could be a water electrolysis plant generating separate H₂and O₂ gases as stored energy, and then conversion module 574 thenconverts the H₂ and O₂ in a fuel cell to make electricity with furtherconversion from DC to AC. In another embodiment, air compression orflywheel storage can be employed in storage module 573 and then energyreleased and converted into electrical power via a rotational toelectrical alternator in conversion module 574. Instantaneous powertransferred from the second electrical network 500 power sources intothe first electrical network 500 is metered by power measuring device540.

The smart grid or first electrical network in accordance with thisillustrative embodiment includes at least one remotely accessibledatabase 586 providing real-time pricing information relating to thefirst electrical network 510 including regional time-of-use electricitypricing and regional demand forecast information for electrical powersupplied by the first electrical network 510. In this embodiment,pricing information is provided by a communication link 588 from thefirst electrical network 510 to the second electrical network 500.Bidirectional data may be exchanged between the first electrical networkprovider and the second electrical network with databases 585 comprisinga plurality of distinct databases and information centres 586 and 587utilised for storing and periodic update of information for the purposeof consumption and remuneration. Communication link 588 may be viainternet network, wireless link or any other suitable datacommunications link. In another embodiment, the first electrical network510 is able to access and store in database 587 via communications link588 usage data relating to the consumption or supply of electrical powerby the second electrical network, these data being provided by powermeasuring devices 535 and 540.

As would be appreciated, power measuring devices 535 and 540 may bedistinct or part of the same device. Furthermore, power measuringdevices may be of accumulation type or capable of time stampingsubstantially instantaneous power flow and providing commerciallyacceptable time-of-use (TOU) function.

Second or end-user electrical network 500 further includes a controller580 having access by communications link 588 to pricing information data586 as it relates to the first electrical network 510 and electricalpower consumption and supply information relating to the secondelectrical network 500 which may be stored in external database 587 orlocally as required. In this illustrative embodiment, controller 580provides intelligent control and assessment of the various system dataas will be described to provide a power transfer schedule for operationof the second electrical network 500. In one example, controller 580 isoperable to provide an end-user with the lowest time-of-use cost ofelectricity for consumption based on time dependent load 570. In anotherexample, controller 580 is operable to provide the highest time-of-useprovision cost for injection of electrical power from the secondelectrical network 500 into the first electrical network 510.

Referring now to FIG. 7, there is shown a schematic representation ofstandard electrical and gas supply networks 600 as is known in the priorart. An electrical smart grid provides a first electrical network 650via distribution networks 625 and 645 and a hydrocarbon fuel networkprovides distribution network 614 and 633. End-users generalised as 610,615, 620, 630, 635 and 640 each contain electrical consumption blocks611, 616, 621, 631, 636 and 641, respectively. Furthermore, eachend-user comprises gas-consumption blocks 613, 619, 624, 634, 639 and644, which are decoupled in their primary function from the electricalconsumption blocks. That is, gas consumption is typically used forthermal energy creation as exemplified by direct heating and or hotwater generation. In other examples, the gas supply is provisioned by anend-user local gas-source located on site at the end-user location. FIG.7 schematically depicts the first electrical network 650 spatiallydistributed by interconnections 625, 622, 617, and 612 and further bynodes 645, 642, 637 and 632. Similarly, the combustible gas network 605is supplied to a plurality of end-users by distribution interconnections614, 618, 623 and 626 and further by nodes 633, 638, 643 and 646.

The combustible gas supply includes, but is not limited to, methane,propane, coal-seam gas, natural gas, shale-gas, biogas or anycombination or blend as appropriate.

Throughout the specification a “gas supply network” is defined toinclude a regional supply network based on pipeline infrastructureoperable to provide combustible gas to a plurality of end-users withinthe region. A gas supply network will typically have associated with ita gas supply retailer responsible for cost recovery from end-userssupplied by the gas supply network.

Referring now to FIG. 8, there is shown a schematic representation ofthe information interchange between smart grid participants 705 and anend-user 790. The smart grid electrical first network generates andprovides electricity by collective action of smart grid participants705, namely, generators 720, electrical transmission networks 730 andretailers 750. As has been discussed previously, a centralisedelectricity market operator 710 schedules and dispatches an entire smartgrid solution to provide electrical energy that substantially matches aforecasted aggregate end-user 790 demand. The forecasted time-dependentdemand profiles and cost of generation profiles are calculated for eachregional zone comprising the smart grid. Information is provided by thesmart grid participants as shown by two way or bidirectional dataexchanges 760, 765 and 770 between the generators 720, electricaltransmission networks 730 and retailers 750 respectively and the marketoperator 710. These bidirectional data exchanges permit real-timefeedback and control of the smart grid operation.

The smart grid 705 is able to provide end-users 790 with one-wayobservable data 775 directly from the market operator 710 in the form ofpricing information as has been previously described. In accordance withillustrative embodiments which will be discussed below, this informationenables end-users 790 to provide a modified demand response to the smartgrid that provides cost advantages to the end-user as well as schedulingand demand management advantages to the smart grid.

Referring now to FIG. 9, there is shown a system for the transfer ofelectrical power between a first electricity network 820 and a second orend-user electricity network 800 according to an illustrativeembodiment. In this illustrative embodiment, first electricity network820 is a smart grid which provides access to pricing information in theform of a regional time-of-use price and demand forecast information810. The forecast look-ahead information 805 spans a useful time windowand has sufficient resolution for an end-user to manage an advantageousand timely response. The first electrical network 820 is distributed 840locally to the end-user electrical network 800. The electrical powertransfer between the first electrical network 820 and the end-userelectrical network 800 is metered with power measuring device 845.

The end-user is further supplied by gas-grid 830 as distributed viapressurised pipeline 825. In another example, the end-user may haveaccess to biofuel gas production or alternatively or additionally toon-site containerised gas stores. The gas-fuel is metered by flowmeasuring device 835 supplied by gas grid 830 interconnector 825.Measuring devices 845 and 835 provide secure metering of consumption andmay provide time-of-use logging of the respective electricity and gasconsumed. In this embodiment, electrical power measuring device 845 iscapable of TOU logging and integrated with an electricity retailer forTOU tariff pricing. The measuring devices 835 and 845 may also beenabled with communication to the relevant retailer for the supply ofgas and electricity respectively for account transaction settlement.

The end-user electrical network 800 is further enabled with agas-to-electricity on-site power generator 850 that can be used tosupply energy to an end-user electrical load 880. In accordance withthis illustrative embodiment, a controller 870 acquires the firstelectrical network forecast 810 via data communications link 890 andinstructs the end-user system to source switch between the firstelectrical 820 network and on-site power generation 850. Selection ofthe electrical power source in this embodiment is achieved via acontrollable transfer switch 860. Optionally, the controller 870 maysupply on-site electrical power to the first electrical network 820which is similarly metered via power measuring device 845 for acommercial transaction benefiting the end-user. Also, the controller maysimultaneous connect the end-user load 880 to both the first electricalnetwork 820 and the on-site power generator 850.

As noted previously, the end-user site may not have access to piped gasnetwork distribution and rely on refillable bottled gas fuels including,but not limited to: compressed natural gas (CNG) or liquefiedgas-storage including liquefied propane gas (LPG) or liquefied naturalgas (LNG).

Referring now to FIG. 10, there is shown a schematic representation ofan ensemble or collection of end-users 910, 920, 930 and 940 each havingan end-user electrical network interacting with a first electricalnetwork 900. The electrical distribution network of the first electricalnetwork 900 for each end-user is shown as 912, 922, 932 and 942, witheach end-user metered via TOU power measuring devices. Similarly, thepressurised gas-grid 905 is distributed to the end-users via gaspipelines 911, 921, 931 and 941 each metered by the appropriateretailer.

The first electrical network time-dependent forecast 960 for therelevant regional zone comprising the smart grid is made available by aremote and real-time access database 965 and accessed or broadcast tothe end-users by an information network 950 comprising data connections914, 924, 934, and 944. The forecast is processed by each end-userscontroller for scheduling an appropriate demand response and selectionof electrical source to service the specific end-user demand. Eachend-user, in general, has differing time-dependent load requirements913, 923, 933 and 943. In this embodiment, the forecast look aheadinformation 964 is the first electrical network forecast 960 which maybe provided either by the market operator or the specific retailersupplying each end-user. The instantaneous end-user load profiles 913,923, 933 and 943 reflect the individual time dependent end-user loadswhich are not accounted for via an estimated aggregate load profile bythe first electrical network forecaster.

Referring now to FIG. 11, there is shown a system for the transfer ofelectrical power between a first electrical network 820 and a second orend-user electrical network 1000 according to another illustrativeembodiment. In this example, end-user electrical network 1000 explicitlysupplies on-site generated electrical power 1070 by transfer switchmatrix formed by transfer switches 1075 and 1035 to the first electricalnetwork 820. Metered electric power consumed from the first electricalnetwork 820 is metered via measuring device 1050, while power suppliedor delivered to the first electrical network is metered via measuringdevice 1040 and is connected via distribution network 1030. It ispossible measuring devices 1040 and 1050 are time-of-use tariff enabled.

The load 1080 can consume electrical power from at least one of thefirst electrical network 1045 or the on-site gas-to-electricityconverter 1070. The end-user controller 1090 controls 1080 the second orend-user electrical network system 1000 to advantage the end-user as astrategic price taker as will be described more fully below. Controller1090 further supplies and receives signals via control lines 1085, forexample signals for controlling the state of power flow for switches1075 and on-site generator 1070 set point.

Referring now to FIG. 12, there is shown a schematic representation of asystem for transfer of electrical power between a first electricalnetwork and multiple end-user electrical networks organised in districtsaccording to an illustrative embodiment. This embodiment illustrates thelarge scale adoption of individual systems for the transfer ofelectrical power as discussed previously.

In this illustrative embodiment, the first electrical network 1105 isdistributed by branch networks 1110, 1115, 1162 and 1163 and thenfurther locally distributed by local networks 1125, 1130, 1135 and 1136.The local districts 1180, 1190 and 1120 are part of a regional zone of asmart grid having access to a first electrical network database 1170providing publically accessible pricing information in the form of aforecast time-of-use price 1171 by telecommunications or data links1172, 1173 and 1174. This information is then accessed by each uniqueend-user and relied on to control the transfer of electrical powerbetween the first electrical network 1105 and each individual end-usernetwork. In another embodiment, districts 1180, 1190 and 1120 may belongto different regional zones within a smart grid and would thereforeaccess the appropriate pricing information relevant to that zone. Thegas-grid 1150 is similarly distributed into the districts viapressurised pipelines 1155, 1152, 1156 and 1157, feeding local networks1160, 1161, 1159 and 1158.

Each district 1180, 1190 and 1120 has a collection of end-users withinstantaneous time-dependent loads, for example, 1181, 1182, 1191, 1121,1122 and 1123. The estimated demand forecasted and then dispatched bythe market operator does not include any awareness of the instantaneousdetailed local load densities of each end-user. The variance in end-userloads is represented by the grey-scale indexing of FIG. 12.

The first electrical network 1105 is optimised for the specific meandemand averaged over the entire regional zone in accordance with thedemand forecast. As is apparent from FIG. 12, each individual end-user'sdemand will likely differ from the mean demand. However, in accordancewith this illustrative embodiment, individual end-users are able tomatch their first electrical network time-of-use consumption to theavailable first electrical network supply with the difference in eachend-user's required demand being supplemented or exclusively switched toa lower cost on-site electricity generation as the case may be.

Referring now to FIG. 13, there is shown a detailed schematicrepresentation of a system for transferring electrical power between afirst electrical network 1205 and an end-user network coupled to boththe first electrical network 1205 and a combustible gas supply grid1210.

The first electrical network pricing information or forecast 1290 isacquired by data acquisition module 1215 which provides the forecastedtime-of-use pricing for off-take from the first electrical network 1205.The cost of the metered gas 1230 provided by gas grid 1210 may becalculated in accordance with the gas-retailer tariff and provides alocal power generation cost 1225 based on the achievable load andefficiency of the on-site power generation plant. The time dependentcost difference schedule Δ(t) (calculated as the time dependent pricedifference between 1220 and 1225) is input into the controller 1275which includes a data processor for generating control signals forcontrolling the second or end-user electrical network's demand response.In this illustrative embodiment, the controller is configured to providethe local load (ie LOCL) 1245 with the lowest cost of on-demandelectrical power.

By providing a cost based decision for time dependent events, the localload 1245 will always be supplied with the lesser cost of electricitysupplied by the first electrical network 1205 or generated on-demand bythe high efficiency gas-to-electrical power generation plant, comprisingthe high efficiency ICRE Engine (HE-Engine), AC-DC Generator/Alternatorand DC-AC converter. The gas meter 1230 may be a smart meter enablingretailer remote access for on-site time logged consumption. Theelectricity consumption meter 1265 may also be a smart meter enabled forretailer access of time-of-use consumption. Second network instantaneouspower measuring devices 1240 and 1250 provide signals 1235 for closedloop control of the end-user electrical network by the controller 1275.

Transfer switches 1260, 1270 and 1255 may be controlled by either wiredsignals or wireless communication links 1280. For example, if theend-user load at a time t demands P_(e) kW of power, then the demand orpower transfer schedule generated by the controller 1275 will configurethe second electrical network's configuration to supply the lowest costsource of electricity to the load 1245. For example, the firstelectrical network's time-of-use price 1290 may exceed that of theon-site generation cost which as a result initiates the on-site powergeneration plant and supplies the generated power to the load 1245 byclosed transfer switches 1270 and 1255.

The first electrical network 1205 may remain connected to the load 1245by closed transfer switch 1260. In order for minimal or no disruption,the on-site generator may be configured to match the active linefrequency and phase of the first electrical network 1205, which isachieved by at least one fast transfer switch or a phase matchinginverter. The on-site power generation plant may optionally seek tooperate at a higher load and inject the surplus power not consumed bythe load 1245 into the first electrical network 1205. The surplus poweris then metred for reimbursement to the end-user by the electricityretailer by use of the secure metring device 1265. An optional heatrecovery electricity converter (HREC) may be used to improve the on-sitegeneration efficiency and thus lower the on-site generation cost.

In one embodiment, the controller as referred to previously implements apower transfer schedule which controls the switching between electricalpower sources at the end-user and further is operative to supplyelectrical power from the end-user whether stored or generated by anon-site generator as will be described below.

Referring now to FIG. 14, there is shown a graph of pricing information1300 in the form of forecast time dependent wholesale cost or pricingfor a regional zone, namely South Australia, in a smart grid, namely theNational Energy Market (NEM) of Australia, for in this case a 1-day lookahead. The running average price 1310 over the specified time forecastwindow is a regional wholesale cost of $90/MW_(e)h. This is the forecastpool price set by the market operator for the NEM smart grid which inthis example is the Australian Energy Market Operator (AEMO). Retailersbid for purchase of power blocks against this incontestable pool price.As can be seen from an inspection of FIG. 14, peak demand times aregenerally between 6 pm-9 pm which in general coincides with peak timepool pricing. As can be seen by inspection of FIG. 14, pricing anomaliessuch as events 1315 also occur requiring the retailers to adopt hedgingstrategies to minimise their exposure to the market. In general, lowregional electricity demand attracts low pool spot prices, as shown byevent 1305.

Referring now to FIG. 15, by decomposing the actual end-user coststoward the various market participants (eg as shown in FIG. 2), thepercentage of wholesale regional cost can be used to determine orcalculate pricing information in terms of a forecast end-usertime-of-use price that will be applied by a retailer to an end-user.This is depicted in FIG. 15 which shows the calculated retailtime-of-use price 1405 set by the retailer based on, or as convertedfrom, the regional wholesale forecast pool price 1305 set out in FIG.14. As such, an end-user would therefore calculate or receive directlyfrom their electricity retailer the forecast time-of-use pricing asshown in FIG. 15 with, in this example, the average TOU price 1410 overthe time period depicted of AU$0.3616/kWh. Relating the wholesale marketregional spot price to the actual regional retail price provided to anend-user is shown by comparing pricing events 1305, 1310 and 1315 withsimultaneous retail events 1405, 1410 and 1415, shown over an exemplary3 day period in this case.

In view of known gas consumption pricing at the end-user site, a localelectricity generation cost can be calculated using the knowngas-to-electricity conversion efficiency η_(gen). As before, if the gaspricing is stated in terms of the multiplier M_(gas) above the wholesaleregional gas price [$/Joule]_(WS), where

$M_{gas} = \frac{\left\lbrack {\$/{Joule}} \right\rbrack_{LOC}}{\left\lbrack {\$/{Joule}} \right\rbrack_{WS}}$

then FIGS. 16, 17, 18 and 19 show the possible demand responses possibleover the forecasted window for various M_(gas) and η_(gen).

Referring now to FIGS. 16 to 19, there are shown graphs of thecalculated forecast retail price as illustrated in FIG. 15 as comparedto calculated thresholds for switching between the first electricalnetwork (ie the smart grid) and on-site generation of power to supply ormeet the on-site demand or load requirements for various assumed valuesfor M_(gas) and η_(gen). These thresholds are then used by thecontroller to determine a power transfer schedule for operativelyswitching between the first electrical network and the on-sitegeneration at the second or end-user electrical network over a forecastperiod.

FIG. 16 shows a threshold cost 1505 for on-site generation of[$/kW_(e)h]_(LOC)=AU$0.239/kW_(e)h for η_(gen)=32.5% and M_(gas)=3.5. Inthis example, for power consumption corresponding to pricing events1510, the power transfer schedule would schedule the generation of powerby the on-site generator whereas for power consumption corresponding topricing events 1515 the power transfer schedule would supply from thefirst electrical network on the basis that the supply cost from thefirst electrical network falls below threshold cost 1505. As would beappreciated, the power transfer schedule consists of a timed sequence ofswitching events as implemented by the controller. As an example, at 12am on day 2 the power transfer schedule would switch supply from thefirst electrical network to on-site power generation on the basis thatthe cost for on-site generation is now less than the now increased costof supply from the first electrical network.

FIG. 17 shows a threshold cost for on-site generation of[$/kW_(e)h]_(LOC)=AU$0.296/kW_(e)h for η_(gen)=35% and M_(gas)=5. Inthis example, for power consumption corresponding to pricing events1610, the power transfer schedule would schedule the generation of powerby the on-site generator whereas for power consumption corresponding topricing events 1615 the power transfer schedule would supply from thefirst electrical network on the basis that the supply cost from thefirst electrical network falls below threshold cost 1605. As would beappreciated, the power transfer schedule consists of a timed sequence ofswitching events as implemented by the controller. As an example, atapproximately 7.30 am on day 1 the power transfer schedule would switchsupply from the first electrical network to on-site power generation onthe basis that the cost for on-site generation is now less than the nowincreased cost of supply from the first electrical network.

FIG. 18 shows a threshold cost for on-site generation of[$/kW_(e)h]_(LOC)=AU$0.172/kW_(e)h for η_(gen)=45% and M_(gas)=3.5. Inthis example, for power consumption corresponding to pricing events1710, the power transfer schedule would schedule the generation of powerby the on-site generator whereas for power consumption corresponding topricing events 1715 the power transfer schedule would supply from thefirst electrical network on the basis that the supply cost from thefirst electrical network falls below threshold cost 1705. As would beappreciated, the power transfer schedule consists of a timed sequence ofswitching events as implemented by the controller. In this example,where both η_(gen) and M_(gas) are favourable to the end-user, given thehigh gas-to-electricity conversion efficiency and price of gas, it canbe seen that the power transfer schedule would leave the on-site powergeneration on during the entire time period depicted as the cost foron-site generation is always less than the cost of supply from the firstelectrical network.

FIG. 19 shows a threshold cost for on-site generation of[$/kW_(e)h]_(LOC)=AU$0.230/kW_(e)h for η_(gen)=45% and M_(gas)=5. Inthis example, for power consumption corresponding to pricing events1810, the power transfer schedule would schedule the generation of powerby the on-site generator whereas for power consumption corresponding topricing events 1815 the power transfer schedule would supply from thefirst electrical network on the basis that the supply cost from thefirst electrical network falls below threshold cost 1805. As would beappreciated, the power transfer schedule consists of a timed sequence ofswitching events as implemented by the controller. As an example,between approximately 10.00 pm-6.00 am on day 1 the power transferschedule would switch supply from the on-site power generation to thefirst electrical network given the low cost of supply from the firstelectrical network as compared to the cost of on-site power generation.

As would be appreciated by those of ordinary skill in the art, use ofon-site electrical power generation when combined with pricinginformation provided by the first electrical network allows the end-userto advantageously minimise their power costs. As can be seen from theabove examples, it is advantageous for a significant portion of theforecast window to utilise on-site power generation. Not only does thisprovide a price advantage to the end-user, from the perspective of thefirst electrical network having end-users who are able to generateon-site power will function to reduce peak and anomalous pricing eventsmore generally as these are essentially removed from the consumptionprofile of that end-user.

Economic Analysis

A life-cycle analysis may be used in order to evaluate the economicperformance of a power transfer or switching system as has beenpreviously described. The life-cycle cost of a system comprises theinitial cost in addition to the lifetime cost of maintenance andoperation discounted to the present time. The life-cycle benefit is thetotal value of all the energy produced over the lifetime of the powertransfer system similarly discounted to the present time. Costs andbenefits for each operational year are projected and then discountedback to the year of installation to obtain the present value. Thepresent value of the benefits may then be compared to the present valueof the costs.

In order to carry out the economic calculation the following assumptionsare made:

-   -   i. r=interest rate    -   ii. N=Lifetime of the system in years    -   iii. g=savings escalator    -   iv. CI=Initial Capital of investment    -   v. OM & TI Operation, Maintenance & Insurance first year

The economic viability of the system is thus determined by thecomparison of the costs to the benefits. The net present value (NPV) isthe difference between the present value of the power transfer systemlifetime resulting benefits (PV_(B)) and the present value of the cost(PV_(C)) generated by acquiring and operating the system over its entirelifetime, such that

NPV=PV_(B)−PV_(C)

Therefore, an economically viable power transfer system is one thatgenerates benefits over the system lifetime that exceed the resultingtotal cost over the same period, necessarily requiring the NPV>0. Forthe case where the benefit equals the cost, then the system breaks even.Conversely, for the case wherein NPV<0, then the power transfer systemis uneconomic as the benefits generated over the entire lifetime willnot cover the invested cost. In the following, the Net Present Value ofLifetime System Cost PV_(C) and the Net Present Value of Lifetime SystemBenefit PV_(B) will be calculated and compared.

Net Present Value of Lifetime System Cost

System cost includes the initial investment capital (CI) needed toacquire and install the system plus the Insurance (TI) and Operation &Maintenance (OM) Costs. The present value of lifetime system cost,PV_(C) is then given by:

${PV}_{C} = {{CI} + \frac{\left( {{{OM}\&}\mspace{11mu} {TI}} \right).{CI}}{CRF}}$

Where, OM & TI=Sum of the Maintenance, Property Tax & Insurancepercentage multiplier for annual payments; and CRF=Capital RecoveryFactor. The Capital Recovery Factor (CRF) is used to discount futurepayments to the present and tabulated as:

${CRF} = \frac{{r\left( {1 + r} \right)}^{N}}{{r\left( {1 + r} \right)}^{N} - 1}$

where N is the year of service for the system.

Net Present Value of Lifetime System Benefit

Lifetime system benefits can be calculated by defining the First YearBenefits (X_(O)) which is the price of the energy produced by the systemat its first year of operation. In order to calculate the present valueof X_(O), it must be calculated considering the electricity priceescalation and the inflation. It is possible to obtain the present valueof the lifetime benefits (PV_(B)) by simply multiplying the First YearBenefit X_(O) by a single calculated parameter Mg, representing thebenefits present value multiplier. That is,

PV _(B)=M_(B)X₀

The Net First Year Energy Savings X_(O) can thus be used to determinethe lifetime benefit of the system.

X _(O)=(First Year Energy Savings)−(Maintenance & Insurance Cost)

The net present value benefit multiplier M_(B) is used to calculate thepresent value benefit PV_(B). It can be accounted using the followingexpression:

$M_{B} = {\frac{1 + g}{r - g}\left\lbrack {1 - \left( \frac{1 + g}{1 + r} \right)^{N}} \right\rbrack}$

Present Value Multiplier at different system lifetimes taking intoaccount a fixed interest rate of (typically 7%) and an escalation ratefor energy cost g=10%.

Referring now to FIG. 20, there is shown an example NPV analysis of asmall P_(gen)=5 kW_(e·)h generation plant with CI=(5kW_(e·)h)*($3.0/W_(e)) and a grid off-take price of $0.48/kWh as afunction of system lifetime. NPV analyses are also shown for gridoff-take prices of $0.15/kWh, $0.32/kWh and $0.65/kWh in FIG. 20.Assuming the on-site generation can produce 50% of the total end-userdaily demand of P_(tot)=25 kW_(e·)h, it is found that pay-back ispossible between 2 and 5 years depending on the gas fuel costs for ageneration efficiency of η_(gen)=35%.

On Site Gas-to Electricity Generators

There are a number of options available for an end-user for on-siteelectrical power generation based on combustible or hydrocarbon gas. Aswould be appreciated, there are several methods available for convertingthe potential chemical energy of a hydrocarbon fuel into electricalenergy. For example, the stored chemical energy within the hydrocarbonfuel (eg methane) may be chemically converted into another species suchas hydrogen gas. In this example, the hydrogen may be used forcombustion or used directly as a component fuel for a hydrogen-oxygenfuel cell.

Reforming of methane into hydrogen is, however, a relatively complexprocess and can suffer from poor conversion efficiency. In fact, mosthydrocarbon gas fuel cells typically require a reforming step prior toobtaining sufficient hydrogen gas for a H₂/O₂ fuel cell. While the H₂/O₂fuel cell is extremely efficient in producing electrical energy, theprior reforming step is likely to be only of poor or moderateefficiency, with a large proportion of the CH₄ being burnt in thereforming process (eg solid oxide fuel cell reforming process).

Yet another method is the combustion of methane within a gas-firedturbine or micro-turbine. These high speed combustion turbines producerelatively high rotational speeds and are well suited to large scalepower plants (eg open cycle gas-turbines and closed cycle gas-turbines)when the rotating shaft is coupled to an alternator. Thegas-to-electrical conversion efficiencies of micro-turbines when coupledto electrical generation may be relatively high 35-40%, and cantherefore offer an advantage in this respect. The large amount of heatrejected from micro-turbines of this type can also be recovered viaheated water or secondary steam powered electrical generation, however,it is relatively challenging to improve the fundamental efficiency ofthe combustion turbine beyond 40% on current technologies.

In another example, the on-site generator may be a natural gas-firedinternal combustion reciprocating engines (ICREs). It is noted that thecapital cost of fuel cells are at present 5-10 times those ofreciprocating engines, however, extremely low operating noise ispossible. Present day micro-turbines have a considerably high capitalcost and require a high level of maintenance making this technologybetter suited to larger applications of 100 kW or more. Furthermore,their output exhaust gas temperatures are significantly higher than anequivalent reciprocating engine and may include harmful emissionsproducts as compared to ICREs.

Although, ultimately the choice of gas-to-electricity on-site generationwill be dependent on the end-user requirements, the use of ICRE's fordomestic requirements is presently indicated as it represents a maturetechnology having a number of advantages including:

-   -   (i) low maintenance;    -   (ii) low capital cost;    -   (iii) high efficiency;    -   (iv) high reliability and durability;    -   (v) mature technology;    -   (vi) small form factor,    -   (vii) used in many parallel applications (e.g., automotive); and    -   (viii) a well-defined technology roadmap for incremental        improvements well beyond the next 20 years.

As referred to above there are constant and incremental improvements inmaterials and technology that provide ICRE's with improved efficienciesespecially when coupled to electromotive generators with near idealconversion efficiencies. For example, linear free-piston engines coupledto linear generators offer the potential of high gas-to-electricityconversion efficiency. Modifications to the standard 4-stroke Otto andMiller cycles include multi-stroke engines with gas expansion scavengingto also improve overall efficiency.

As would be appreciated, ICREs offer significant advantages for domesticapplications. However, any technology which is capable of convertinghydrocarbon fuel into electrical energy is contemplated to be within thescope of the present invention. A gas-to-electricity conversionefficiency of greater than or equal to 20% is preferable with furtherbenefits if the efficiency is greater than or equal to 30% or evengreater than 40%. Conversely, if the cost of gas feedstock issufficiently low or provided to an end-user at sufficiently low cost oras a by-product or even waste, then the efficiency of thegas-to-electricity conversion module may be even less than 20% and stillprovide an economical benefit to the end-user.

Referring now to FIG. 21, there is shown on the left hand side adetailed schematic representation of the gas-to-electricity converter2100 illustrated in FIG. 6 (see item 560) in the form of an ICRE 1160and an alternator 2165. In this illustrative embodiment, ICRE 1160 is astationary engine 2160 supplied by oxidant 2110 and hydrocarbon fuel2105. The air and fuel may be pre-treated in 2115 and 2120,respectively, for the purpose of improving the combustion efficiencywithin ICRE 1160. The optionally enhanced air 2130 and fuel 2125 aresupplied as controlled feedstock for consumption by the ICRE 1160.Combustion by-products from the ICRE 1160 are ejected as exhaust gas2155 which may be further used for energy recovery (eg thermoelectricenergy scavenging and/or thermo-mechanical scavenging forturbo-charging).

An optional flywheel 2162 may be coupled to the rotating shaft of theICRE 1160, with the rotating shaft further delivering rotational energyto an electrical alternator and/or generator 2165. The electrical outputof the generator 2165 may be alternating current or substantially directcurrent. In this illustrative embodiment, the firstrotational-to-electrical device is an alternator producing asubstantially sinusoidal single or poly-phase alternating current output2170. Sensor 2166 detects the shaft rotational speed which is used as afeedback signal to a control module 2140 to stabilise or lock to aspecified rotational shaft speed the shaft rotational speed of ICRE1160.

Control module 2140 is provided with a data communications link 2141which can set and interrogate the operation of the plant 2100. Thecontrol module 2140 further functions to control via signals 2150 thefiring sequence of the pistons, valves and internal timing of ICRE 1160.In preference, but not limited to, is the use of electromechanicalcylinder valve actuation devices for the intake and exhaust sequences.These are electromechanical actuators for opening and closing thecylinder head intake/exhaust sequences and may be controlledelectronically and without mechanical-to-mechanical linkages to furtheroptimise the engine efficiencies across a wide range of operatingconditions.

In one mode of operation, the output electrical frequency ofgas-to-electricity converter 2100 matches the line frequency of theend-user second electrical network and the first electrical or supplynetwork. This requires synchronisation of the shaft rotating speed andphase to the first electrical network. For direct coupling of thealternator to the shaft rotation, the specific rotational speed and thespecific number of pole pairs within the alternator will determine theoutput frequency 2170.

Referring again to FIG. 21, on the right hand side there is shown agraph 2180 of the alternating current frequency as a function of shaftrotation produced by the alternator for varying numbers of pole pairsN_(p). FIG. 21 shows the cases of 2176 (N_(p)=2), 2175 (N_(p)=4), 2174(N_(p)=6), 2173 (N_(p)=12), 2172 (N_(p)=24) and 2171 (N_(p)=36).Therefore, depending on the range of ICRE rotation speed for acceptableload response and efficiency, an appropriate number of poles may bechosen to match to f₀=50 Hz or any desired output frequency.

In other illustrative embodiments, there is provided a tuneable supplyto match the dynamic end-user demand. It is preferable then for the ICREto produce electrical power decoupled from the line frequency of theend-user or second electrical network load. Embodiments of this type arediscussed with reference to FIGS. 22 and 23 a.

Referring now to FIGS. 22 and 23 a, there are shown schematicrepresentations of systems for transfer of electrical power between afirst electrical network 2290 to a second electrical or end-user network2200, 2300 further incorporating supply by a combustible gas supply grid2295 for use by an on-site gas-to-electricity converter 2250. The firstelectrical network electricity consumed by the second or end-userelectrical network is metered by time-of-use energy meter 2285 and thegas supply network consumption is metered by measuring device 2235. Agas pressure regulator 2230 supplies hydrocarbon fuel to the end-usergas-to-electricity converter 2250. As would be appreciated, gas sourcedfrom gas supply network 2295 may also be consumed for other purposeswithin the end-user plant. The end-user electrical load 2286 is bothresistive and reactive and is further metered in real-time by measuringdevice 2287. Control and telemetry signals are provided to the systemcomponents to and from the controller 2260 by wireless or wiredconnections 2255, 2261 and 2236. Catalytic conversion 2245 of exhaustproducts 2265 reduces at least one of the oxides of carbon, sulphur andnitrogen in the expelled gas stream. Thermal recovery 2270 provides heatenergy 2275 for further utility by the end-user.

Referring again to FIG. 22. ICRE 2250 is fed with ambient air 2205,which may be processed further 2210 and injected 1220 into the ICRE 2250intake manifold. Hydrocarbon fuel 2240 is provided to fuel processor2215 and then injected 2225 into the ICRE 2250 intake manifold. Theair-to-fuel ratio is controlled by the controller 2260 and modifiers2210 and 2215. For example, modifiers may be programmed to alter the airto fuel mixture ratio depending upon the electrical load demanded of thegenerator 2280 which in turn mechanically loads the ICRE 2250 outputshaft. Both the TOU pricing of electricity 2285 and gas unit pricing2235 forms pricing information that is used by the controller 2260, aswill be described in detail later, to further provides control signalswithin the end-user network in the form of a power transfer schedule toprovide the required demand response.

A transfer switch 2289 is electrically connected to the end-user load2286 and the first electrical network metered supply at point 2288. Atadvantageous price taking events, the end-user on-site generation plantor gas-to-electricity converter provides energy to the second electricalnetwork load 2286, and optionally if the power generated is in excess ofspecific real-time demand of load 2286, the excess energy is capable ofbeing delivered into the first electrical network via metering device2285.

In this example, where electricity is being supplied by the end-userelectrical network to the first electrical network, a feed-in TOU tariffmay be applied to credit the end-user's account with the relevant firstelectrical network retailer for electricity supplied to the firstelectrical network. Therefore, unlike renewable energy sources such assolar where the amount of electricity generated is based onenvironmental considerations and not the price of electricity at thattime, a power transfer system in accordance with the embodimentsdescribed above may participate advantageously for on-demand and highvalue TOU pricing events in the first electricity or supply network.This clearly provides an advantage to the end-user who is now able todynamically interact with the first electricity network according to astrategy that minimises the end-user's costs.

Comparing FIGS. 22 and 23 a it is apparent the end-user generators 2250deliver a phase synchronised alternating current to the second networkconnection point 2288. Synchronisation may be performed by using themeasured phase information provided by the measuring device 2287 andoperation of controller 2260.

As would be appreciated, the power transfer schedule determining theswitching between electrical power supply sources may result inextremely frequent switching that could even occur on an hourly rate orless. This may necessitate the rapid synchronisation of the on-sitegeneration electrical alternating current phase to the first electricalnetwork supply phase. FIG. 23a illustrates one way to achieve thiscapability should it be required. As shown in FIG. 23a , the alternator2280 is further coupled to an AC-to-DC conversion device 2305 thatproduces direct current (DC) output 2315. This DC output then feeds aninverter module 2310 which produces line frequency output with a highconversion efficiency. The DC-to-AC inverter 2310 produces extremelyhigh conversion efficiency between electrical modes and providesintegrated synchronisation of the output AC 2320 to the first electricalnetwork line frequency. In this manner, the original AC signal may beconverted to an AC signal whose phase and timing matches that of thefirst electrical network.

Referring now to FIG. 23b , there is shown a schematic setting out thefunctional components of a gas-to-electricity converter 3900 along thelines described above. Hydrocarbon fuel or combustible gas 3910 issupplied to ICRE 3920 delivering rotational energy 3925 at an efficiencyof η_(ICRE). The power and torque curves 3950 for the ICRE are afunction of shaft rotation speed, ω. The ideal efficiency is typicallyobtained in the power band 3955. An alternator 3930 receives therotational energy 3925 and converts this into electrical energy 3935,producing time varying voltage 3960 at frequency 3965 which depends on ωand the number of pole pairs N_(p) within the alternator. The alternatoroperates with conversion efficiency η_(Alt). The output electricalenergy 3935 is then converted to the desired first electrical networkfrequency via frequency-to-frequency converter 3940. The ICRE cantherefore track the electrical load at an optimal speed and or engineperformance map (eg, lean burn operation at partial load), therebytracking fuel consumption to the electrical load. The output electricalpower 3945 is time varying voltage 3970 of constant frequency 3975 (eg.50 or 60 Hz) and the output voltage range 3970 is governed by thefrequency-to-frequency 3940. If the system 3900 provides an outputfrequency that matches advantageously the phase relative to the firstelectrical network and further provides an output voltage 3970 slightlygreater than the voltage provided by the first electrical network 2290,then the transfer switch 2289 can be optional. Thefrequency-to-frequency converter 3940 operates with conversionefficiency η_(F2F). The overall efficiency is therefore,

η_(Gen)=η_(ICRE)·η_(Alt)·η_(F2F)

In another embodiment, a fast transition time transfer switch 2289 maybe actuated by the controller 2260 that can connect the on-sitegenerator output 2320 to the active connection point 2288 within theend-user network 2300.

Referring now to FIG. 23c , there is shown schematic circuit diagramdepicting the electrical functionality of a fast transition timetransfer switch 2289 as illustrated in FIGS. 23a and 22. Transfer switch2289 comprises two parallel circuit elements, namely, a (i) fast switchon time and medium impedance path; and a (ii) slower switching time on avery low impedance path.

Electro-mechanical contactors have relatively slow switching times butoffer very low impedance connections across closed switch elements andvery high isolation for open switch elements. To provide rapid switchingso that alternating current may be synchronised in phase between theon-site power generator and the first electrical network supply, thefast response upper path comprising normally-closed contactor switch2355 and semiconductor switch module 2360 is implemented. The lowerelectrical path comprises a slower but low impedance electromechanicalnormally-open switch module 2365. The electromechanical switches 2356and 2368 are actuated by coils 2371 and 2377. Digital signal control ofthe coils is provided by solid-state switches 2372 and 2376. The mainhigh current handling solid-state switch 2360 is controlled via controlsignal 2361. Mechanical switch actuators 2357 and 2367 are governed byenergised relay coils 2370 and 2375.

Initially, consider the switch in high isolation configuration, suchthat, switch 2356 is closed, solid-state switch 2360 is high impedanceand switch 2368 is in open state. A first electrical network phasedetector (ie provided for example by energy measuring device 2287)provides information to the controller 2260. Assume the on-sitegenerator is enabled and providing a valid operating point. Azero-crossing detector connected to the on-site generator may be used toinitiate a closure of the transfer switch 2289 by comparing the phase tothe first electrical network and then providing control signals toactuate the main solid-state switch 2360. Switch 2360 responds withsufficiently fast response time so that accurate phase synchronisationis possible.

The current initially flows through switches 2355 and 2360, whereas slowswitch 2365 remains substantially in an open-state. Current flowing inthe upper portion of the circuit 2362 enables the electromechanical coil2377 to be energised and thus begins closure of 2368. Once switch 2368is fully closed, it introduces a low impedance circuit for current topredominately flow between connections 2350, 2366, 2368, 2381 and 2380.The upper circuit may then be isolated by appropriate control signals.The low impedance path may be also put into a high impedance state bydc-energising the electromechanical actuating coil 2377 by switching offthe solid-state switch 2376.

As would be appreciated, transfer switch design as described above anddepicted in FIG. 23c provides one non-limiting example of a fast andnon-disruptive power transfer between electrical supply sources. Aswould be further appreciated, the control and switching arrangements maybe extended to multi-phase power network such as three phase 3 φ power.For 3 φ operation there would be in one embodiment individualsub-controllers for each separate load of the 3 φ second electricalnetwork operating in accordance with the principles described throughoutthe specification.

Referring once again to functional component diagram of FIG. 6, thegas-to-electricity conversion efficiency of the on-site power generationplant 560 is now discussed.

Both the on-site power generation efficiency η_(Gen) and cost ofhydrocarbon fuel ($/Joule) determine the cost of producing on-siteelectricity. Referring now to FIGS. 24, 25 and 26 there are shown graphsof calculations of the available on-site electricity generation cost perkW·hr ($/kWh) as a function of the power generation efficiency η_(Gen)and delivered cost of fuel at different scales. The fuel cost isprovided as an end-user retail cost [$/Joule]_(LDC) delivered to theend-user site with multiplicative value above a wholesale cost ofnatural gas.

In these examples, the regional wholesale cost of natural gas is takenfrom data applicable to South Australia in March of 2013. Converting theavailable chemical energy of CH₄, into electricity generation cost inunits of $/kW_(e)h is then plotted as the contours in FIG. 24. Asdiscussed previously, present day technologies for gas-to-electricityconversion are expected to provide efficiencies in the range30%≦η_(Gen)≦45%. Based on this, it can be seen that low cost on-siteelectricity generation can be provided for commercially accessiblenatural gas costs in the range of:

$3 \leq \frac{\left\lbrack {\$/{Joule}} \right\rbrack_{LOC}}{\left\lbrack {\$/{Joule}} \right\rbrack_{WholeSale}} \leq 8$

In view of the smart grid operation as discussed previously, therelevant comparison is between the retailer provided first electricitynetwork cost (eg see FIG. 15) and the calculated on-site cost as plottedin FIGS. 24, 25 and 26. By way of example, an operating point 2405 shownin FIG. 24 provides sufficient economic advantage for on-site powergeneration that is directly competitive to off-peak first network TOUpricing (see FIG. 15 where off-peak retail to end-user spot price isapproximately $0.18/kWh) for gas cost multiple of 4 over wholesale andη_(Gen) of 35%.

As can be seen from above, on-site gas-to-electricity conversionprovides an economical alternative to the generation of electricalpower. When this is factored into a power transfer system that allows anend-user to schedule switching between a normal electrical supplynetwork and an on-site source of electrical power this allows anend-user to configure an advantageous demand response in the context ofpricing for the end-user. Furthermore, the end-user is enabled as astrategic price-taker from the first network TOU tariff pricing. Anextreme, but equally valid case is the end-user as a first preferenceburning on-site fuel for exclusive electricity consumption, using thefirst electrical network as a back-up source of electricity if generatorplant 560 becomes unavailable or end-user load demand power is in excessof the nominal capacity of the gas-to-electricity converter 560.Accordingly, the end-user is provided with a clear decision path fordynamic source switching based on cost of electricity generationcomparison between a retail TOU first electrical network cost and anon-site generation cost.

Partial Load Generation Efficiency

Referring now to FIG. 27, there is shown a graph of the generationefficiencies a function of electrical load for an ICRE basedgas-to-electricity converter using methane fuel as previously disclosed.FIG. 27 demonstrates the potential departure from the ideal cases offull-load efficiencies presented in FIGS. 24 to 26. As depicted in theinset schematic, these reductions in generation efficiencies may beaccounted for by adopting a generator configuration incorporatingmultiple smaller capacity generators. Depending on the circumstances, a10% de-rating of the nominated ICRE efficiency may be achieved forpartial load conditions by adopting a multiple generator configuration.

FIG. 27 shows three representative curves for parallel electricalconnected generators 2710, namely, G₁, G₂ . . . G_(N), supplying powerto a generalised load 2730. Each generator current 2715 is summed atnode 2720, providing power 2725 to load 2730. Curve 2750 is for N=8parallel connected 5 kW_(e) generators, showing the achievable systemefficiency delivered to a load 2730 for a given power demand requested2705.

For example, referring to curve 2750, a single 5 kW_(e) generatorachieving a full-load efficiency of η_(Gen)=35% produces a generationefficiency performance between 20%≦η_(Gen)≦35% under partial loadoperation of 30% to 100%. Curves 2755 and 2760 show the performance forfive parallel connected (N=5) P_(Gen)=10 kW_(e) generators each with afull load efficiency of η_(Gen)=37.5% and N=3 P_(Gen)=20 kW_(e) andfull-load η_(Gen)=40% respectively. As is apparent, multiply connectedsmaller scale generators may provide improved efficiency underconditions ranging from partial load to full-load condition as the casemay be for end-user power consumption.

Improved performance may additionally be achieved by running the one ormore electricity generator at near to, or at full load, by supplying theexcess electrical energy to the first electrical network for subsequentreimbursement by a retailer for feeding in of electricity as previouslydescribed. This is another strategy that enables the on-site powergenerator to operate at maximum efficiency under end-user partial loadconditions.

Referring now to FIG. 28, there is shown a contour plot of on-siteelectricity generation cost as a function of partial electrical load andmethane fuel cost for a given gas-to-electricity full load conversionefficiency of η_(Gen)=35% for P_(Gen)=10 kW_(e). The methane fuel costis given as an end-user retail cost multiple over the wholesale gascost.

FIG. 29 is similar to FIG. 28 and is a contour plot of on-siteelectricity generation cost as a function of partial electrical load andmethane fuel cost for a given gas-to-electricity full load but for aconversion efficiency of η_(Gen)=40%. The methane fuel cost is given asan end-user retail cost multiple over the wholesale gas cost. Similarly,FIG. 30 is a contour plot of on-site electricity generation cost as afunction of partial electrical load and methane fuel cost for a givengas-to-electricity full load but for a conversion efficiency ofη_(Gen)=45%. As can be readily seen, the on-site generation costsincrease slightly but are still well within off-peak first networkconsumption costs.

Clearly, improvements in the on-site gas-to-electricity conversionefficiency will directly translate to reduced on-site generation costswhich in this exemplary embodiment are well below the available off-peaktariff rates as of 2013 in South Australia. This offers a potentialsolution to large scale electric vehicle adoption which would otherwiseplace a massive increase in peak demand on smart grid or supplynetworks. At present, electric vehicle charging is viewed by electricalsupply networks as a significant threat as it is expected to severelyexacerbate peak first electrical network demand. The use of agas-to-electricity converter that forms part of the end-user electricalnetwork and which is controllable in accordance with pricing informationassociated with the first electrical network provides a comprehensivesolution to on-demand electric vehicle charging at low cost that issubstantially decoupled from the first electrical smart grid network.

Retailer Function for Advantageous End-User Price Taking

Yet a further commercial benefit of the adoption of on-sitegas-to-electricity power generation system is the ability for retailersto offer new products to end-users for providing distributed powergeneration to the first network. Referring now to FIG. 31, there isshown a schematic representation of a smart grid in accordance with anillustrative embodiment where an end-user or second-electrical networkparticipates within the smart grid or first electrical network andreceives reimbursement for stabilising regional peak demand.

In this exemplary embodiment, an end-user 3130 is reimbursed forproviding electricity at strategic times to the first network by use offeed-in power measuring device (MD_2) 3132. Measuring devices 3131 and3132 may be operated as a single TOU unit or may be distinct andoperated by two separate retailers 3105, 3125. Retailers may specialisein supplying power to an end-user (eg retailer 3105) or alternatively,or in addition to, a retailer may offer a service or contract to anend-user that provides access to the smart grid market operator 3150 (egretailer 3125). The first electrical network is provided electricalpower by a plurality of scheduled generation types 3110, transmitted toa regional area by electrical network 3115 and distributed within aregional cluster by network 3120.

As the distribution network costs are charged as a package cost by theretailer 3105 providing the electricity, it may be possible tocircumvent double costs for accessing the distribution network whenfeeding in electricity 3132. In one example, feed-in retailer 3125 wouldaggregate distributed generation sources as provided by a plurality ofindividual end-users 3130 and provide a collective capacity to marketoperator 3150 based on assessment of the collective generation capacityof the individual end-users. Transactions 3140 between the marketoperator 3150 and the feed-in retailer 3125 are then distributed to theend-users 3130.

As would be appreciated, the above described embodiments provide anend-user with the capability to provide on-demand electricity generationthat is of high value to the first electrical network which may beutilised by the smart grid to address peak demand problems. In thismanner, the end-user becomes an active participant of the firstelectrical market choosing to supply electricity to the smart grid inaccordance with contractual arrangements with the feed-in retailer or atprice points determined by the end-user. This clearly provides the smartgrid with an unparalleled degree of flexibility in not only addressingexpected peak demand events but also fluctuations in demand that are notforecasted.

It is expected that as the adoption of on-site gas-to-electricityconversion combined with access to pricing information increases thenthe variation in demand as seen by the smart grid will reduce. Thisreduction in variability allows the smart grid to better forecast andprovision for the expected demand including utility costs and further toreduce the amount of hedging and futures contracts required. This isexpected to then result in a net decrease to the retail cost of supplyof electricity to individual end-users.

On-Site Power Generation System

Referring now to FIGS. 32, 33, 34 and 35, there is shown a number ofillustrative embodiments of power transfer systems involving the supplyof power by a first electrical network to a second electrical networkwhere pricing information associated with the supply of electricity bythe first electrical network is accessible by the second electricalnetwork and further where the second electrical network includes on-sitecapability for electricity generation based on the supply of combustiblegas.

Detailed operation of further illustrative embodiments is presented inFIGS. 32, 33, 34 and 35. Central to the operation of these embodimentsis the strategic interconnection and operation of the on-sitegas-to-electricity generation when compared to a smart grid firstnetwork electricity cost. That is, the end-user interacts with the smartgrid as a strategic time-of-use price taker by coupling advantageouslyto an alternative energy source that is not subject to rapid time-of-usefuel costs.

Referring now to FIG. 32, there is shown a functional block diagram ofthe operational components of a power transfer system 3200 according toan illustrative embodiment. Electricity from a smart grid firstelectrical network 3240 is supplied to the end-user via at least onepower measuring devices. In one example, a TOU tariff may be supplied toan end-user via dual accumulation meters 3245, 3250 including a timedoff-peak meter and a normal meter. Alternatively, one meter may beconfigured for take-off of electrical power from the grid and the otheris configured for electrical power fed-into the grid from the end-userelectrical network. In another example, meters 3245, 3250 may beconfigured as a single device. In yet another example, one or bothmeters 3245, 3250 may both be TOU equipped providing accurateinstantaneous power flow and time logging. Measuring devices 3245 and3250 may further be configured to communicate information with aretailer (eg via wireless broadcast) and provide data communication tocontroller 3201. Internal control and telemetry signals such as 3293 areinterfaced to the controller 3201.

In accordance with this embodiment, pricing information in the form of aforecast of the first electrical network time-of-use pricing is acquiredby the cost comparison device 3210. A gas consumption measuring device3225 provides meter flow information of gas consumed 3226 by theend-user electrical network from the gas supplier 3229 via pressureregulator 3228. The gas meter 3225 may further be enabled as a smart-gasmeter which communicates with a gas retailer. The meter 3225 may furtherprovide updated gas tariff pricing information which may form inputinformation to the procedure for determining a power transfer scheduleas will be described below.

Functional block 3210 depicts the process for determining the powertransfer schedule. The cost per Joule ([$/Joule]_(LOC)) is calculated by3220 (which also includes a forecast or TOU price tariff) and is usedfor direct comparison with the forecasted time-of use cost ofelectricity 3211 provided by the first electrical network 3240. A storedforecast estimate of TOU pricing is then used to generate arepresentative first electrical network time dependent price forconsumption providing data 3214 which can then be directly compared tothe on-site electrical generation cost 3216 that is based on the gasprice and the gas-to-electricity efficiency. The on-site electricitycost 3216 and first electrical network consumption cost 3214 is used todetermine the power transfer schedule or on-site demand response basedon the price comparison 3215 carried out.

Controller 3201 generates demand response or power transfer schedule inthis illustrative embodiment by a data processor 3231 and scheduler3232. The power transfer schedule is dynamically updated in accordancewith updates to pricing information from the first electrical networkand/or pricing information provided by the gas supply network where thisis available. Typically, this pricing information is in the form of aperiodically updated forecast. The forecast 3205 may have minute rangeresolution and span hourly, daily or weekly look-ahead. The regionalforecast 3205 is updated regularly and provided by the smart grid marketoperator and in this illustrative embodiment is updated in 15 minsperiods as is the case for the Australian NEM. Internal end-user networkinstantaneous power measuring devices 3275 and 3290 may also provideoptional telemetry to the controller 3201.

In this illustrative embodiment, communication between all components ormodules within the end-user network 3200 is via interface module 3237providing digital representation of signals to the data processor 3231and scheduler 3232 as appropriate. The gas-to-electricity converter 3233consumes metered fuel 3227 and generates rotational energy and heatenergy. The rotational energy is coupled to the alternator 3234 at ashaft rotational frequency f_(ω) producing an alternating current offrequency f_(C) depending upon the number of pole pairs N_(p) as hasbeen previously discussed. This AC waveform is then injected into thefrequency-to-frequency (F2F) converter 3235.

The F2F comprises an AC-to-DC conversion plus filtering with the DCsignal then fed into a DC-to-AC converter (also known as an inverter)3235. The F2F converter enables the gas-to-electricity converter(comprising 3232, 3234 and 3235) to track the electrical load bymatching the engine speed and fuel consumption. The output AC powermeasured by 3290 resulting from the final stage output 3230 thereforeremains synchronised with the first electrical network regardless of theengine rotation speed. The output module 3235 further integratessynchronisation of the AC output with a desired reference phase andfrequency measured at either measurement modules 3245, 3250 or 3290. Thesource switching is controlled by power transfer switches 3285, 3260,3255 and 3270. As would be appreciated, all the transfer switches maynot be necessary for a reduced system implementation (eg refer system2300 of FIG. 23a ) and can be used for protection.

FIG. 32 shows a highly flexible switch matrix that provides improvedflexibility for isolating and interconnecting combinations of theend-user load 3280 (which is referenced to ground potential 3281), theon-site generator and/or first electrical network implemented usingcontrollable transfer switches 3285, 3260, 3255 and 3270. The on-sitegeneration power connects the end-user load 3280 and first networksupply at a common feed point 3265. Electrical and communication links3292 transfer control or telemetry signals 3291 to the controller 3201.

In this illustrative embodiment, power transfer system further includesheat recovery module 3236 operable to recover heat energy fromgas-to-electricity converter 3233. In one embodiment, heat recoverymodule 3236 is a heat-to-electricity converter operable to convertrecovered heat energy to electricity. The heat-to-electricity 3236converter provides further scavenged electrical power that can be addedto the electrical output stage 3235. This increases the overallconversion efficiency of the gas-to-electricity conversion process. Inanother embodiment, heat recovery module 3236 is operable to generateheated water for end-user consumption. In yet another embodiment, heatrecovery module 3236 converts heat energy to mechanical work for useon-site by in one example generating steam for a turbine or Stirlingengine or alternatively or additionally to be used in an expansionengine to aid in producing rotational energy.

Referring now to FIG. 33, there is shown a functional block diagram ofthe operational components of a power transfer system 3200 according toan illustrative embodiment. In this embodiment, power transfer system3200 includes two separate loads, namely, a separate off-peak load 3365and an all time-of-use normal load 3375, with both loads referenced to acommon potential 3370. Each load is optionally monitored by a power andphase measuring device 3360 and 3355, as well as a generator powermonitor 3320. Data or control signals are transferred from individualcomponents within the end-user system 3200 to the controller 3201 viawired or wireless links such as for example 3330. Transfer switches3325, 3315, 3255, 3335 and 3350 offer one example configuration forenabling simultaneous cross connection of on-site power source and firstnetwork supplying either or both end-user loads or feeding power intothe first electrical network 3240. The electrical current summing points3340 and 3345 are explicitly for each of the respective load types.

Referring now to FIG. 34, there is shown a functional block diagram ofthe operation components of a power transfer system according to anillustrative embodiment. In this embodiment, power transfer systemincludes a single time-of-use power measuring and metering device 3465capable of bi-directional power flow measurement and time logging thatis connected between the first electrical network 3480 and the end-userelectrical network 3400. Power measuring and metering device 3465 isfurther capable of remotely sending data in relation to compiledend-user time-of-use consumption time logs to a retailer or certifieddata aggregator for billing purposes. The first electrical network 3480provides electrical power or optionally allows feed-in of on-site powergeneration from the end-user electrical network which is metered bydevice 3465. Similarly, the gas metering and measuring device 3450 canbe enabled to remote gas consumption data to a gas or energy retailerand to the controller 3201 as appropriate. Metered combustible gas 3455is delivered to the gas-to-electricity converter 3233 and provided bygas supplier or network 3485.

The power transfer system is provided with data access 3415 to anexternal database 3405. The data link 3415 can be wireless or via aninternet protocol link as customarily provided by an information networkprovider. The database 3405 provides time dependent pricing informationwhich is uploaded by the end-user to 3430 obtained directly from datasource 3410 in the form of a first network forecast time-dependent priceand demand look-ahead profile which accurately represent the marketoperation of a regional zone applicable to the end-user electricalnetwork 3400.

In this illustrative embodiment, the database information is provided bya specific internet protocol (IP) address for periodic and frequentpublic access. The database information is also preferably provided bythe market operator or the electricity retailer providing service to theend-user 3400. The regional forecast data 3410 is acquired and stored3425 in the end-user site in digital form for processing by dataprocessor 3231. While explicitly shown for clarity, it is understoodthat functional components 3415, 3425, 3435, 3440, 3445 may in practicepart be integrated into controller 3201. Each time-dependent firstelectrical network TOU consumption cost forecast data 3430 is compareddirectly to the on-site gas-to-electricity generation cost 3440calculated via algorithm 3445. Therefore, the end-user performs astrategic determination based on the price difference between the timedependent TOU forecast price from the first electrical network and theon-site gas-to-electricity on-site generation cost. The end-user system3400 further comprises power measuring devices 3466 and 3475 along withoptional transfer switches 3467, 3468 and 3470. Control and telemetrysignals are aggregated 3461 and interfaced 3460 to the controller 3201.The explicit current summing node 3469 can be configured with on-sitegenerator output 3225 having a slightly higher output voltage in phasewith the first electrical network, so that power flow is explicitlydirected from the on-site generation plant into at least one of the load3480 or 3490.

The two signals 3435 and 3440 are fed into the comparator with theresult forming the basis of a future time-dependent demand response orpower transfer schedule and is input into the data processor 3231 andprocessed as described previously. Further detailed computational methodinformation is described in PCT Application No PCT/AU2014/000605 titled“ELECTRICAL POWER CONTROL METHOD AND SYSTEM”, filed on 12 Jun. 2014 andwhose contents are hereby incorporated in their entirety.

The end-user network 3400 is enabled to perform a calculation via thecomputational methods described within the controller 3201 to provide ademand response schedule to automatically initiate time-dependentend-user system configuration. The operation of the system 3400 seeks tominimize TOU cost of electricity consumed by an end-user load 3480 ormaximise feed-in payback for on-site on-demand power generated withinthe end-user network.

In another embodiment, pricing information relating to the supply of gasmay be provided by the gas retailer or alternatively a combined marketoperator as has been described previously in relation to the supply ofelectricity. In one example, the end-user network can acquire, via IP orwireless access or other equivalent communication means, informationrelating to the time dependent gas cost tariff or wholesale price.

As would be appreciated, power transfer system 3400 enables an end-userto forecast and therefore estimate gross gas fuel consumption overextended periods, such as on a yearly basis. This allows an end-user tonegotiate forward bulk gas fuel consumption contracts with a gasretailer providing service to the end-user. The end-user can furtherguarantee consumption of all gas purchased under the forward contract asany excess gas not consumed by the end-user electrical load can bescheduled via scheduler 3232 to burn on-site and feed in advantageouslyto the first electrical network as previously described. This allows theend-user to advantageously schedule on-site generation of electricity ata reduced cost to the end-user in the process also alleviating the peakdemand of the first electricity network. Additionally, the end-user mayfeed in on-site generated electricity into the first electrical networkat a price premium to the end-user in the process also beneficiallyproviding additional capacity to the first electrical network.

In accordance with the embodiments described here, the end-user mayparticipate in new market products which reward on-demand peak TOU powergeneration providing service that is not possible using renewable energysources alone to the benefit of the market operator and to the energysupply system as a whole.

Referring now to FIG. 35, there is shown a functional block diagram ofthe operation components of a power transfer system 3400 according toyet another illustrative embodiment. As would be appreciated, one of thebenefits of end-user system 3400 consuming hydrocarbon gas-fuel foron-site electricity generation by an ICRE arrangement is that theexhaust by-product gas from direct combustion within the ICRE providessignificantly lower green-house gas (GHG) emissions to the atmosphere,namely, carbon dioxide (CO₂), carbon monoxide (CO) and nitrogen oxides(NO_(x)). Sulphur oxides (SO_(x)) are negligible and offer a distinctadvantage over coal and oil derived fuels used in traditionalelectricity generation. As on-demand on-site power generation offers asmaller carbon emission footprint compared to an equivalent power unitconsumed from the smart grid, there exists in addition an opportunityfor rewarding the end-user for reducing carbon-emissions.

In this illustrative embodiment, a further emission reduction device3510 in the form of a catalytic converter and carbon emission measuringdevice 3520 may be optionally introduced. It is also possible for theemission monitoring device 3520 to be enabled for contestable loggingand subsequent inclusion in end-user reward. If the CO₂ or emission canbe monitored then a price for emitting or cost saving for emissionreduction (ie CO₂ avoided) may be provided. The end-user would beexpected to produce less CO₂ per kW generated than a large scale fossilfuelled plant due to the operation of the catalytic converter 3510.

Referring now to FIG. 36, there is shown a functional block diagram ofthe operational components of a power transfer system for an end-usernetwork 3600 according to a further illustrative embodiment. In thisembodiment, power transfer system is interfaced with two informationdatabases 3405 and 105. The first electrical network forecast data isprovided by public access of information database 3405 and accessed viaIP or wireless or equivalent means whereas the retailer database 105(gas and/or electricity) may be accessed directly via at least one of acommunication method comprising IP, wireless, or digital broadcast. Theretailer 3613 may utilise a third party data aggregator 3614. Themetered TOU consumption data 3625 of the end-user 3600 is transmittedsecurely via unique identifier signal 3612 to the data aggregator 3614uniquely representing the specific end-user 3600.

The retailer 3613 may also transmit via the data aggregator 3614 to allthe end-users a broadcast signal that is loaded into the smart meter3625 incorporating an updated TOU tariff structure and optionally aforecasted TOU price or a demand response event calendar. The controller3605 is supported by communications interfaces 3606 and 3607 providinginput/output operations to control paths 3627 and database links 3610,3626 and 3611. The end-user may also upload information to the gas andelectricity retailers relating to consumption and for the case ofon-site electricity generation would provide time dependent powergeneration fed into the first electrical network. The bi-directionalexchange of information between the end-user and the gas and electricityretailers enables synchronisation of assets and enables efficient and/ortailor made forecasts to be provided to the network users.

The input first electrical network 3480 with access line 3620 is meteredinto the power transfer system 3600 via smart meter 3625, which can beisolated from the second electrical network by transfer switch 3630. Theend-user load 3642 can be supplied electricity by the first electricalnetwork 3480 or by on-site power generation system 3690. Controllablepower transfer switches 3640 and 3650 can connect to at least one of thefirst electrical network and on-site power plant 3690. In-situmonitoring devices measure power 3641 and 3651 and transmit theinstantaneous power to the controller 3605 for closed-loop control. Asmart gas meter 3656 provides consumption data for take-off gas flow3655 from a gas provider 3485 with gas consumption data remotely sent tothe controller 3605 and or the gas retailer 105. The operation asdescribed herein for the control device calculation of on-sitegeneration cost.

The gas-to-rotational energy conversion module 3653 (eg, an ICRE)provides rotational energy-to-electricity conversion device 3654 whichin turn feeds a frequency-to-frequency converter 3652. The powermanagement module 3657 provides synchronisation and control of thegas-to-electricity converter plant 3690. Controller 3605 executescontrol logic to calculate the threshold cost for determining aninternal demand or power transfer schedule for the end-user network 3600as previously described.

Referring now to FIG. 37, there is shown an example output of thecontroller comparator showing a threshold cost for on-site powergeneration. A first electrical network price forecast time-dependentlook-ahead is acquired against which the comparator determines whetherto off-take electricity from the grid or in preference to reduce cost byburning gas fuel via on-site gas-to-electricity converter. Regions belowthe threshold cost, in this case $71.85, indicate the smart gridprovided electricity TOU costs less than on-site generation cost and inaccordance with the power transfer scheduler the controller schedulesthe power transfer system at these specific times to off-take from thefirst electrical network smart grid. For first electrical network TOUpricing events exceeding the on-site generation cost, the power transferschedule schedules the end-user network to provide gas-to-electricitygenerated power to the end-user load or supply electricity to the firstelectrical network.

Referring now to FIG. 38a , there is shown a graph of the cost avoidedover the forecast time window (ie a 24-hour look-ahead for thisexample). The total cost avoided is defined as the integrated cost abovethe on-site threshold cost of FIG. 37. FIG. 38a shows the cumulativecost avoided by the end-user network for specific choice of gas fuelcost and on-site gas-to-electricity generation efficiency. Curves 3815,3810 (and 3805 are for the cases of η_(Gen)=45, 50 and 60% calculatedagainst the time forecast window provided in FIG. 37 (whereη_(LOC)=η_(Gen) at the location). Clearly, even for nominal operation ofthe smart grid (i.e., without anomalous pricing events), there aresubstantial cost savings to gained by an end-user adopting embodimentsof power transfer system as described above.

Referring now to FIG. 38b , there is shown a schematic representation ofa smart grid 705 or first electrical network including an ensemble of Nparticipating end-users 790 with each end-user participating withtailored demand response as has been previously described that producesa substantially real-time information feedback path 3850 to retailers750. This provides an opportunity for the end-users to match their firstnetwork electricity demand to the available supply. As would beappreciated, a smart grid electrical network operating in accordancewith these principles is in contradistinction to the present lessefficient methods where the smart grid attempts to in real-time matchfirst electrical network supply to the collective but estimated end-userdemand of each second electrical network coupled to the smart grid.

A brief review of the above embodiments indicates the systems andmethods described above indicates that they enable an end-user to accessan additional source of on-site electricity and to further choosebetween generation sources substantially on the basis of time-of-usecost and the cost of on-site generation, thereby allowing the end-userto reduce their costs and further in some embodiments to provideelectricity to the supply electrical network.

On-site generation of electricity is based on a supply of low-coststored energy in the form of combustible gas which is able to provide ahigh efficiency conversion between the stored energy in the gas toelectricity as required. Accordingly, the supply of combustiblehydrocarbon gas supplied via a gas distribution network whether it bedirect or through storage means provides an end-user site with on-demandaccess to stored energy for the specific purpose of conversion intoelectricity at a time of choosing of the end-user. This enables anend-user to strategically switch between electrical grid supplied andon-site electricity generation sources for the primary purpose ofreducing instantaneous or time-of-use electricity cost.

Accordingly, the above described embodiments relate to coupling ordecoupling of two time-dependent energy systems or sources to anend-user. The first energy source is characterised by it beingprovisioned to meet an instantaneous demand on the part of a collectionof end-users and as such its capacity varies dynamically to providesubstantially instantaneous electrical power as required. The firstenergy source is the first electrical network or smart grid (FN). Thesecond energy source is characterised by it being a stored energy sourcein that the amount of energy is predetermined and there is nodisadvantage to not using the energy at a given time as the energy mayremain stored. In this case, the stored energy source is combustible gas(SN) which may be provided directly to an end-user by storage means inthe form of gas supply pipeline network or containcrised gas. In anotherembodiment, the stored energy source is a fossil fuel such as petrol,diesel, kerosene or other standard liquid hydrocarbon. A first timedependent network FN represents the smart grid electricity price, whichis made available to the end-user or second electrical network. Inaccordance with the previously described embodiments, the end-user timedependent network (EUN) interacts with both the FN & SN with a pricetaking response by the power transfer system and method described.

The EUN network represents the end-user system and inherent end-userdemand. In addition to the EUN, there is a coupling of yet a furthertime dependent electrical power source representing on-site powergeneration. The on-site power generator of the EUN has access to thestored energy source provided by the SN and is available for on-demandfor conversion into useful product (ie, electricity). The on-sitegenerated electricity may be consumed by the end-user load or in someembodiments provided as electrical energy to the first electricalnetwork. The decision for the end-user network (EUN) to consume aspecific time dependent cost of electricity from either the firstelectrical network or via on-site generation may be based on lowest costprovision to the EUN.

Conversely, the EUN can also maximise the time dependent price to supplysurplus power generated within the EUN to the FN (ie, supply on-sitegenerated electricity to the first electrical network). The EUN thusrequires timely electricity source switching to reduce the transactioncosts for the EUN. In accordance with the above embodiments, the EUN isable to advantageously improve both price taking from the firstelectrical network and/or provide a deficit or surplus of energy byaccessing an on-site generation source fuelled by the SN stored energysupply. As such, the EUN is able to advantageously partially or fullydecouple itself from the first electrical network's time dependence byuse of on-site generation plant based on the stored energy source (SN).As the on-site generation plant has access to stored energy in the formof fuel which is provided at a specific cost to produce a given unit ofelectricity upon conversion, the decoupling may be implemented inaccordance with a power transfer schedule to provide a competitive costas compared to the standard time-of-use cost provided by the FN.

As would be appreciated, the time dependence of the fuel cost of thecombustible gas varies on a time scale that is substantially longer thanthe cost variation time scale of the electricity cost provided by thefirst network. Accordingly, the SN stored energy is providedsubstantially at fixed cost over an extended time period whereas the FNtime-of-use pricing is volatile over much shorter time constants. Assuch, access to the SN stored energy by an end-user and the capabilityof converting this stored energy to electricity on-site functions todampen or smooth the volatility of the FN which also provides advantagesto the electricity supply market as a whole as it reduces the risk ofretailer exposure to spot price volatility which would be expected inturn to reduce the general end-user time-of-use tariff.

Those of skill in the art would further appreciate that the variousillustrative logical blocks, modules, circuits, and method stepsdescribed in connection with the embodiments disclosed above may beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Accordingly, embodiments may beimplemented to achieve the described functionality in varying ways foreach particular application.

For a hardware implementation, processing may be implemented within oneor more devices or systems, including but not limited to, applicationspecific integrated circuits (ASICs), digital signal processors (DSPs),digital signal processing devices (DSPDs), programmable logic devices(PLDs), field programmable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described herein, or any combination asappropriate. Software modules, also known as computer programs, computercodes, or instructions, may contain a number a number of source code orobject code segments or instructions, and may reside in any computerreadable medium such as a RAM memory, flash memory, ROM memory. EPROMmemory, registers, hard disk, a removable disk, a CD-ROM, a DVD-ROM orany other form of computer readable medium. In the alternative, thecomputer readable medium may be integral to the processor. The processorand the computer readable medium may reside in an ASIC or relateddevice. The software codes may be stored in a memory unit and executedby a processor. The memory unit may be implemented within the processoror external to the processor, in which case it can be communicativelycoupled to the processor via various means as is known in the art.

Various aspects of the method and system described above may be computerimplemented. An example computer system is illustrated in FIG. 39 andcomprises a display device, a processor and a memory and an inputdevice. The memory may comprise instructions to cause the processor toexecute a method described herein. The processor memory and displaydevice may be included in a standard computing device, such as a desktopcomputer, a portable computing device such as a laptop computer ortablet, or they may be included in a customised device or system. Thecomputing device may be a unitary computing or programmable device, or adistributed device comprising several components operatively (orfunctionally) connected via wired or wireless connections. Asillustrated in FIG. 39, one embodiment of a computing device 3900comprises a central processing unit (CPU) 3910, a memory 3920, a displayapparatus 3930, and may include an input device 3940 such as keyboard,mouse, etc. The CPU 3910 comprises an Input/Output Interface 3912, anArithmetic and Logic Unit (ALU) 3914 and a Control Unit and ProgramCounter element 3916 which is in communication with input and outputdevices (eg input device 3940 and display apparatus 3930) through theInput/Output Interface. The Input/Output Interface may comprise anetwork interface and/or communications module for communicating with anequivalent communications module in another device using a predefinedcommunications protocol (eg Bluetooth, Zigbee, IEEE 802.15, IEEE 802.11,TCP/IP, UDP, etc). A graphical processing unit (GPU) may also beincluded. The display apparatus may comprise a flat screen display (egLCD, LED, plasma, touch screen, etc), a projector, CRT, etc. Thecomputing device may comprise a single CPU (core) or multiple CPU's(multiple core). The computing device may use a parallel processor, avector processor, or be a distributed computing device. The memory isoperatively coupled to the processor(s) and may comprise RAM and ROMcomponents, and may be provided within or external to the device. Thememory may be used to store the operating system and additional softwaremodules that can be loaded and executed by the processor(s).

Throughout the specification and the claims that follow, unless thecontext requires otherwise, the words “comprise” and “include” andvariations such as “comprising” and “including” will be understood toimply the inclusion of a stated integer or group of integers, but notthe exclusion of any other integer or group of integers.

The reference to any prior art in this specification is not, and shouldnot be taken as, an acknowledgement of any form of suggestion that suchprior art forms part of the common general knowledge.

It will be appreciated by those skilled in the art that the invention isnot restricted in its use to the particular application described.Neither is the present invention restricted in its preferred embodimentwith regard to the particular elements and/or features described ordepicted herein. It will be appreciated that the invention is notlimited to the embodiment or embodiments disclosed, but is capable ofnumerous rearrangements, modifications and substitutions withoutdeparting from the scope of the invention as set forth and defined bythe following claims.

1. A method for controlling the time dependent transfer of electricalpower between a first electrical network and a second electricalnetwork, the first electrical network operable to provide instantaneouselectrical power to the second electrical network located at a location,the second electrical network including electrical generating capacityat the location based on stored energy accessible at the location, themethod comprising: receiving at the second electrical network pricinginformation from the first electrical network, the pricing informationassociated with the future supply of electrical power by the firstelectrical network to the second electrical network; and modifyingsubstantially in real time the transfer of electrical power between thefirst and second electrical networks in accordance with the pricinginformation and electricity demand characteristics of the location. 2.The method for controlling the time dependent transfer of electricalpower as claimed in claim 1, wherein modifying substantially in realtime the transfer of electrical power between the first and secondelectrical network includes the second electrical network generatingelectricity on-site to satisfy the electricity demand characteristics ofthe second electrical network where a cost of generating electricityon-site is less than or equal to a cost of electricity supplied by thefirst electrical network.
 3. The method for controlling the timedependent transfer of electrical power as claimed in claim 1, whereinmodifying substantially in real time the transfer of electrical powerbetween the first and second electrical network includes the secondelectrical network supplying at least a portion of the on-site generatedelectricity to the first electrical network at a reimbursement pricegreater than or equal to a cost of generating electricity on-site. 4.The method for controlling the time dependent transfer of electricalpower as claimed in claim 1, wherein modifying substantially in realtime the transfer of electrical power between the first and secondelectrical network includes storing by the second electrical networkelectricity supplied by the first electrical network or generated by thesecond electrical network to be either employed by the second electricalnetwork or supplied back to the first electrical network at a latertime.
 5. The method for controlling the time dependent transfer ofelectrical power as claimed in claim 1, wherein the stored energy is inthe form of combustible gas.
 6. The method for controlling the timedependent transfer of electrical power as claimed in claim 5, whereinthe combustible gas is stored at the location and is comprised of anyone of: compressed natural gas (CNG); liquefied propane gas (LPG);liquefied natural gas (LNG); or any combination of the above.
 7. Themethod for controlling the time dependent transfer of electrical poweras claimed in claim 5, wherein the combustible gas is supplied by a gassupply network.
 8. The method for controlling the time dependenttransfer of electrical power as claimed in claim 7, wherein modifyingsubstantially in real time the transfer of electrical power between thefirst and second electrical network includes receiving pricinginformation from the gas supply network and including this informationin present and future calculation of generating electricity on-site. 9.The method for controlling the time dependent transfer of electricalpower as claimed in claim 7, further including supplying a forecast ofgas fuel consumption by the second electrical network to the gas supplynetwork.
 10. The method for controlling the time dependent transfer ofelectrical power as claimed in claim 5, wherein the electricalgenerating capacity at the location based on stored energy is an on-sitegas-to-electricity converter.
 11. The method for controlling the timedependent transfer of electrical power as claimed in claim 10, whereinthe on-site gas-to-electricity converter comprises a gas-to-rotationalenergy converter and a rotational energy-to-electricity converter. 12.The method for controlling the time dependent transfer of electricalpower as claimed in claim 11, wherein the gas-to-rotational energyconverter is an internal combustion reciprocating engine (ICRE).
 13. Themethod for controlling the time dependent transfer of electrical poweras claimed in claim 1, wherein the first electrical network is anelectrically interconnected utility-scale grid under the control of amarket operator comprising at least one power generation source and atransmission and/or distribution interconnection network operable tosupply power.
 14. An electrical power control system comprising: a firstelectrical network configured to supply instantaneous electrical powerto a second electrical network located at a location, wherein the firstelectrical network further provides pricing information associated withthe future supply of electricity; a power measuring device for measuringthe demand characteristics of the second electrical network; an on-sitestored energy to electricity converter for converting stored energy atthe location to electricity; and a controller for receiving the demandcharacteristics of the second electrical network and the pricinginformation from the first electrical network and determining a powertransfer schedule for the second electrical network controlling whetherelectricity is to be sourced from the first electricity network or fromthe on-site stored energy to electricity converter.
 15. The electricalpower control system of claim 14, wherein the power transfer schedulefurther controls whether electricity is to be stored on-site by thesecond electrical network.
 16. The electrical power control system ofclaim 14, wherein the power transfer schedule further controls whetherelectricity from the on-site energy to electricity converter is suppliedto the first electrical network.
 17. The electrical power control systemof claim 14, wherein the on-site stored energy to electricity converteris a gas-to-electricity converter based on combustible gas.
 18. Theelectrical power control system of claim 14, wherein the combustible gasis supplied by a gas supply network.
 19. The electrical power controlsystem of claim 14, wherein the combustible gas is stored at thelocation and is comprised of any one of: compressed natural gas (CNG);liquefied propane gas (LPG); liquefied natural gas (LNG); or anycombination of the above.
 20. A method of controlling the interactionbetween first and second power supply systems at an end-user site, thefirst power supply system operable to provide electrical power thatmatches the instantaneous demand of a plurality of end-users includingthe end-user site, and where the second power supply system is based onstored energy where the end-user is able to generate electrical power atthe end-user site from the stored energy at a predetermined time and fora predetermined duration, the method comprising: dynamically switchingby the end-user between the first and second power supply systems inaccordance with a cost benefit analysis based on pricing informationprovided substantially in real time to the end-user by the first powersupply system.
 21. The method of controlling the interaction betweenfirst and second power supply systems as claimed in claim 20, where thestored energy is based on combustible gas.
 22. The method of controllingthe interaction between first and second power supply systems as claimedin claim 21, wherein the combustible gas is supplied by a gas supplynetwork.
 23. The method of controlling the interaction between first andsecond power supply systems as claimed in claim 21, wherein thecombustible gas is stored at the location and is comprised of any oneof: compressed natural gas (CNG); liquefied propane gas (LPG); liquefiednatural gas (LNG); or any combination of the above.
 24. An electricitymarket system comprising: a plurality of electricity generators; adistribution network for distributing electricity generated by theplurality of electricity generators to a plurality of end-users orcustomers of the electricity market; at least one retailer for receivingmonies from the plurality of end-user in satisfaction for theelectricity supplied to an end-user; and a market operator fordetermining a forecast demand and a settled price for the wholesale costof electricity as supplied by the plurality of generators, wherein themarket includes a plurality of end-users each enabled with on-siteelectrical generating capacity based on stored energy and who determinewhether to receive electricity from the electricity market or supplyelectricity to the electricity market based on a cost benefit analysiscarried out by the end-user.
 25. The electricity market system of claim24, wherein the electrical generating capacity based on stored energy isa gas-to-electricity converter based on the supply of combustible gas tothe individual end-user.
 26. The electricity market system of claim 24,wherein the combustible gas is supplied by a gas supply network.
 27. Theelectricity market system of claim 24, wherein the combustible gasincludes combustible gas stored at an individual user's location, thestored combustible gas comprised of any one of: compressed natural gas(CNG); liquefied propane gas (LPG); liquefied natural gas (LNG); or anycombination of the above.
 28. The electricity market system of claim 24,further including an ensemble of end-users having an aggregated on-sitegenerating capacity who are treated as part of the electricitygenerating capacity of the electricity market to manage demandvolatility.
 29. An energy supply system comprising: a stored energynetwork comprising combustible gas supplied to an end-user by a gasdistribution network for supplying combustible gas, the combustible gasmetered by a gas provider and supplied at an agreed gas consumptiontariff structure; and an instantaneous energy network comprising anelectrical distribution network that supplies electricity to the enduser site that is metered by an energy provider at an agreed electricityconsumption tariff structure, wherein the end-user switches betweenelectricity generated on-site from the combustible gas and electricitysupplied by the electrical distribution network to minimise the end-usercosts.
 30. The energy supply system of claim 29, wherein the combustiblegas is gas supplied by a gas supply network to individual end-users. 31.The energy supply system of claim 30, wherein the end-user providesforecast gas consumption demand information to the gas distributionnetwork to negotiate a future gas consumption tariff structure.
 32. Theenergy supply system of claim 27, where the future gas consumptiontariff structure is time and volume dependent.
 33. The energy supplysystem of claim 29, wherein the end-user provides forecast electricityconsumption information to the electrical distribution network tonegotiate a future electricity consumption tariff structure.
 34. Theenergy supply system of claim 33, wherein the future electricityconsumption tariff structure is time and amount dependent.